DeepMoA: method to predict the mechanism of action of cancer drugs

Select data and import libraries

import sys # we require code from other folders
import pandas as pd
import numpy as np
import itertools
import pickle
import os
os.environ['KMP_DUPLICATE_LIB_OK']='True'

import seaborn as sns
import matplotlib.pyplot as plt
CB_color_cycle = ['#EECC16', '#62BB35', '#FDAE33','#208EA3', '#EA4E9D', '#984ea3','#999999', '#e41a1c', '#dede00']
#sns.set_style("darkgrid")

import matplotlib.font_manager as fm
font_files = fm.findSystemFonts()

plt.rcdefaults()
# Go through and add each to Matplotlib's font cache.
for font_file in font_files:
    fm.fontManager.addfont(font_file)
plt.rc('font', family='Roboto')

plt.rc('font', family='Roboto')

plt.rcParams['font.family'] = 'sans-serif'
plt.rcParams['font.sans-serif'] = 'Roboto'

#%config InlineBackend.figure_format='retina'

# pytorch relates imports
import torch
import torch.nn as nn
import torch.optim as optim

# imports from captum library
from captum.attr import LayerDeepLift

# for combobox
from __future__ import print_function
from ipywidgets import interact, interactive, fixed, interact_manual
import ipywidgets as widgets

import plotly.express as px
import plotly.graph_objects as go
import plotly.io as pio

pd.options.display.min_rows = 20000
pd.set_option('max_colwidth', 200)

pd.options.display.max_rows = 20000

pd.set_option('min_rows', 20000)

mac = "/Users/katyna/Library/CloudStorage/OneDrive-Tecnun/"
windows = "C:/Users/ksada/OneDrive - Tecnun/"
computer = windows # CHANGE

sys.path.append(computer + "SparseGO_code/code")
import util
from util import *

%matplotlib inline

#%matplotlib inline

# To make histograms
def histogram(dataframe, color, title, ylabel,n_bins):
    N, bins, patches = plt.hist(dataframe, color=color,bins=n_bins, linewidth=0.1)

    for i in range(0,len(bins)-1):
        if bins[i]<0.05:
            patches[i].set_facecolor(CB_color_cycle[2])

    plt.xlabel("P-value", fontsize=16)  
    plt.ylabel(ylabel, fontsize=16)
    plt.title(title, fontsize=16)
    plt.xticks(fontsize=14)  
    plt.yticks(fontsize=14)
    ax = plt.subplot(111)  
    ax.spines["top"].set_visible(False)  
    ax.spines["right"].set_visible(False)    

inputdir = computer+"SparseGO_code/data/cross_validation_expression/allsamples/" # CHANGE
dir1=computer+"Tesis/Codigo/VariableImportance/"
dir2=computer+"SparseGO_code/results/weights&biases/Expression_MSE_all/" # CHANGE
resultsdir=dir2

gene2id = inputdir+"gene2ind.txt"
cell2id=inputdir+"cell2ind.txt"
drug2id=inputdir+"drug2ind.txt"
drug2fingerprint=inputdir+"drug2fingerprint.txt"
load=resultsdir+"last_model.pt"

onto = inputdir+"ontology.txt"  # CHANGE 
genotype=inputdir+"cell2expression.txt"  # CHANGE 

num_neurons_per_GO = 6 # CHANGE

DeepLIFT

gene2id_mapping = load_mapping(gene2id)
dG, terms_pairs, genes_terms_pairs = load_ontology(onto, gene2id_mapping)
sorted_pairs, level_list, level_number = sort_pairs(genes_terms_pairs, terms_pairs, dG, gene2id_mapping)
layer_connections = pairs_in_layers(sorted_pairs, level_list, level_number) 

cell_features = np.genfromtxt(genotype, delimiter=',')
drug_features = np.genfromtxt(drug2fingerprint, delimiter=',')

drug2id_mapping = load_mapping(drug2id)
cell2id_mapping = load_mapping(cell2id)

num_genes = len(gene2id_mapping)
drug_dim = len(drug_features[0,:])

There are 15015 genes
There are 1 roots: GO:0008150
There are 4184 terms
There are 1 connected components

model = torch.load(load, map_location='cuda:%d' % 0)

model

sparseGO_nn(
  (genes_terms_sparse_linear_1): SparseLinearNew(
    in_features=15015, out_features=25104, bias=True, sparsity=0.0030196221878822263, connectivity=tensor([[    0,     1,     2,  ..., 23721, 23722, 23723],
            [    0,     0,     0,  ..., 15014, 15014, 15014]], device='cuda:0'), small_world=False
  )
  (genes_terms_tanh): Tanh()
  (genes_terms_batchnorm): BatchNorm1d(25104, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
  (GO_terms_sparse_linear_1): SparseLinearNew(
    in_features=25104, out_features=8304, bias=True, sparsity=0.002372788160788691, connectivity=tensor([[  966,   967,   968,  ...,  7047,  7048,  7049],
            [    0,     0,     0,  ..., 25103, 25103, 25103]], device='cuda:0'), small_world=False
  )
  (GO_terms_tanh_1): Tanh()
  (GO_terms_batchnorm_1): BatchNorm1d(8304, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
  (GO_terms_sparse_linear_2): SparseLinearNew(
    in_features=8304, out_features=3684, bias=True, sparsity=0.003911619061964564, connectivity=tensor([[   0,    1,    2,  ..., 3681, 3682, 3683],
            [   0,    0,    0,  ..., 8303, 8303, 8303]], device='cuda:0'), small_world=False
  )
  (GO_terms_tanh_2): Tanh()
  (GO_terms_batchnorm_2): BatchNorm1d(3684, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
  (GO_terms_sparse_linear_3): SparseLinearNew(
    in_features=3684, out_features=1650, bias=True, sparsity=0.007924193070772875, connectivity=tensor([[ 150,  151,  152,  ..., 1641, 1642, 1643],
            [   0,    0,    0,  ..., 3683, 3683, 3683]], device='cuda:0'), small_world=False
  )
  (GO_terms_tanh_3): Tanh()
  (GO_terms_batchnorm_3): BatchNorm1d(1650, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
  (GO_terms_sparse_linear_4): SparseLinearNew(
    in_features=1650, out_features=726, bias=True, sparsity=0.015807663410969196, connectivity=tensor([[ 474,  475,  476,  ...,  711,  712,  713],
            [   0,    0,    0,  ..., 1649, 1649, 1649]], device='cuda:0'), small_world=False
  )
  (GO_terms_tanh_4): Tanh()
  (GO_terms_batchnorm_4): BatchNorm1d(726, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
  (GO_terms_sparse_linear_5): SparseLinearNew(
    in_features=726, out_features=318, bias=True, sparsity=0.03305785123966942, connectivity=tensor([[ 60,  61,  62,  ..., 105, 106, 107],
            [  0,   0,   0,  ..., 725, 725, 725]], device='cuda:0'), small_world=False
  )
  (GO_terms_tanh_5): Tanh()
  (GO_terms_batchnorm_5): BatchNorm1d(318, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
  (GO_terms_sparse_linear_6): SparseLinearNew(
    in_features=318, out_features=120, bias=True, sparsity=0.06981132075471698, connectivity=tensor([[  0,   1,   2,  ...,  93,  94,  95],
            [  0,   0,   0,  ..., 317, 317, 317]], device='cuda:0'), small_world=False
  )
  (GO_terms_tanh_6): Tanh()
  (GO_terms_batchnorm_6): BatchNorm1d(120, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
  (GO_terms_sparse_linear_7): SparseLinearNew(
    in_features=120, out_features=42, bias=True, sparsity=0.2, connectivity=tensor([[ 18,  19,  20,  ...,  21,  22,  23],
            [  0,   0,   0,  ..., 119, 119, 119]], device='cuda:0'), small_world=False
  )
  (GO_terms_tanh_7): Tanh()
  (GO_terms_batchnorm_7): BatchNorm1d(42, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
  (GO_terms_sparse_linear_8): SparseLinearNew(
    in_features=42, out_features=30, bias=True, sparsity=1.0, connectivity=tensor([[ 0,  1,  2,  ..., 27, 28, 29],
            [ 0,  0,  0,  ..., 41, 41, 41]], device='cuda:0'), small_world=False
  )
  (GO_terms_tanh_8): Tanh()
  (GO_terms_batchnorm_8): BatchNorm1d(30, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
  (drug_linear_layer_1): Linear(in_features=2048, out_features=200, bias=True)
  (drug_tanh_1): Tanh()
  (drug_batchnorm_layer_1): BatchNorm1d(200, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
  (drug_linear_layer_2): Linear(in_features=200, out_features=100, bias=True)
  (drug_tanh_2): Tanh()
  (drug_batchnorm_layer_2): BatchNorm1d(100, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
  (drug_linear_layer_3): Linear(in_features=100, out_features=50, bias=True)
  (drug_tanh_3): Tanh()
  (drug_batchnorm_layer_3): BatchNorm1d(50, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
  (final_linear_layer): Linear(in_features=80, out_features=40, bias=True)
  (final_tanh): Tanh()
  (final_batchnorm_layer): BatchNorm1d(40, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
  (final_aux_linear_layer): Linear(in_features=40, out_features=1, bias=True)
  (final_aux_tanh): Tanh()
  (final_linear_layer_output): Linear(in_features=1, out_features=1, bias=True)
)

# Save layers to be analyzed
model_layers = []
model_layers.append(model.genes_terms_sparse_linear_1)
model_layers.append(model.GO_terms_sparse_linear_1)
model_layers.append(model.GO_terms_sparse_linear_2)
model_layers.append(model.GO_terms_sparse_linear_3)
model_layers.append(model.GO_terms_sparse_linear_4)
model_layers.append(model.GO_terms_sparse_linear_5)
model_layers.append(model.GO_terms_sparse_linear_6)
model_layers.append(model.GO_terms_sparse_linear_7)

GO terms info

# Go term names
gene_ontology = pd.read_excel('all_go_terms_info.xlsx')

Get all layers’ GO term with the neuron number

all_terms_ids = {}
all_terms_names = {}
all_layers_non_virtual = {} # store only terms that are part of the layer (remove virtual), those are the important attribuitions
all_layers_non_virtual_names = {}
num_neurons_per_GO = 6

for layer_number in range(len(layer_connections)-1):
    layer_pairs = layer_connections[layer_number] 
    
    terms_ids = []
    names = []
    output_id = create_index(layer_pairs[:,0]) # first 6 neurons correspond to the term with key 0
    
    for term in output_id.keys():
        #name = gene_ontology.loc[gene_ontology['GO_term'] == term].to_numpy()[0,3].replace("_"," ").capitalize()
        name = gene_ontology.loc[gene_ontology['id'] == term].to_numpy()[0,1].capitalize()
        for i in range(1,num_neurons_per_GO+1): # vector que tiene GO:0000038_1, GO:0000038_2 ... GO:0000038_6 y asi luego concatenar con las attributions
            terms_ids.append(term+"_"+str(i))
            names.append(name+" ("+str(i)+")")
    all_terms_ids[layer_number] = np.array(terms_ids)
    all_terms_names[layer_number] = np.array(names)

    non_virtual = [] # store the terms part of that layer
    non_virtual_names = []
    for term in level_list[layer_number+1]:
        nv_name = gene_ontology.loc[gene_ontology['id'] == term].to_numpy()[0,1].capitalize()
        for i in range(1,7):
            non_virtual.append(term+"_"+str(i))
            non_virtual_names.append(nv_name+" ("+str(i)+")")
    all_layers_non_virtual[layer_number] = non_virtual
    all_layers_non_virtual_names[layer_number] = non_virtual_names

All GO terms part of a layer (non-virtual) with their corresponding name and layer number…

real_go_info = pd.DataFrame({"GO_term":[],"Name":[],"layer_number":[]})
for layer_number in range(len(layer_connections)-1):
    layer_go_info = pd.DataFrame({"GO_term":all_layers_non_virtual[layer_number],"Name":all_layers_non_virtual_names[layer_number],"layer_number":(layer_number)})
    real_go_info = pd.concat((real_go_info,layer_go_info))
real_go_info.head()

	GO_term	Name	layer_number
0	GO:0000019_1	Regulation of mitotic recombination (1)	0.0
1	GO:0000019_2	Regulation of mitotic recombination (2)	0.0
2	GO:0000019_3	Regulation of mitotic recombination (3)	0.0
3	GO:0000019_4	Regulation of mitotic recombination (4)	0.0
4	GO:0000019_5	Regulation of mitotic recombination (5)	0.0

Drugs info

def get_compound_names(file_name):
    compounds = []

    with open(file_name, 'r') as fi:
        for line in fi:
            tokens = line.strip().split('\t')
            compounds.append([tokens[1],tokens[2]])
    return compounds

drugs = get_compound_names(inputdir+"compound_names.txt")
drugs.pop(0)

['SMILE', 'Name']

DeepLIFT for VNN

Reference activation… (baseline)

median_cell_features = np.median(cell_features,axis=0) # to use as a reference
median_drug_features = np.genfromtxt(computer+"SparseGO_code/data/glucose_fingerprint.txt", delimiter=',')

Attribution function: sum

def get_layer_attribution(layer_number,input_data,baseline,selected_drug_data):
    dl = LayerDeepLift(model, model_layers[layer_number],multiply_by_inputs = True) # CHOOSE LAYER TO STUDY
    dl_attr_test = dl.attribute(input_data,baseline)
    dl_attr_test_sum = dl_attr_test.cpu().detach().numpy().sum(0) # se suman las attributions para cada sample
    
    attribution_data = pd.DataFrame(np.column_stack((all_terms_ids[layer_number],dl_attr_test_sum)), columns=["GO_term",selected_drug_data[1]])
    attribution_data[[selected_drug_data[1]]] = attribution_data[[selected_drug_data[1]]].apply(pd.to_numeric).round(10)
    attribution_data = attribution_data.loc[attribution_data['GO_term'].isin(all_layers_non_virtual[layer_number])] # only the keep the non virtual terms
    return attribution_data

DeepLIFT for all drugs

attribution_data_all = pd.DataFrame()
# Obtain the top GO terms on all layers for each drug
for selected_drug_data in drugs:
    selected_drug =selected_drug_data[0] # DRUG smile
    selected_drug_features = []
    drug_specific_features=drug_features[drug2id_mapping[selected_drug]] # features of drug
    
    for i in range(len(cell2id_mapping)): # make all combinations of selected_drug and cell types 
        selected_drug_features.append(np.concatenate((cell_features[i], drug_specific_features), axis=None))
    selected_drug_features = torch.FloatTensor(np.array(selected_drug_features))

    # Data for deeplift...
    input_data = torch.autograd.Variable(selected_drug_features.cuda(0))
    
    #median_drug_features = drug_specific_features
    # baseline is the median of the expression data and drug features 
    baseline = torch.FloatTensor(np.concatenate((median_cell_features, median_drug_features), axis=None))
    baseline = torch.reshape(baseline, (1, baseline.size()[0]))
    baseline = torch.autograd.Variable(baseline.cuda(0))
    
    attribution_data_drug = list(map(get_layer_attribution,range(0,len(model_layers)),itertools.repeat(input_data, len(model_layers)),itertools.repeat(baseline, len(model_layers)),itertools.repeat(selected_drug_data, len(model_layers)))) # get the attribution for each layer (map is similar to apply)
    attribution_data_drug = pd.concat(attribution_data_drug) # concatenate attribution of all layers
    
    attribution_data_all = pd.concat([attribution_data_all,attribution_data_drug.iloc[:,1]], axis=1)
    
    print(selected_drug_data[1])
    
attribution_data_all = pd.concat([attribution_data_drug.iloc[:,0],attribution_data_all], axis=1)

attribution_data_all = attribution_data_all.set_index("GO_term")

attribution_data_all.head()

	BRD-K02251932-001-01-3	BRD-K25737009-001-01-2	Nintedanib	bicalutamide	N-[(2R,3S)-2-[[cyclopropylmethyl(methyl)amino]methyl]-5-[(2R)-1-hydroxypropan-2-yl]-3-methyl-6-oxo-3,4-dihydro-2H-1,5-benzoxazocin-8-yl]-1-methyl-4-imidazolesulfonamide	PHA-665752	N-cyclopropyl-3-[3-[[cyclopropyl(oxo)methyl]amino]-1H-indazol-6-yl]benzamide	Ki8751	IPA-3	FAWUGYGEBHAQBU-PPEXNQRJSA-N	...	ML031	Semagacestat	RITA	CDK9 inhibitor	Dasatinib	BMS-536924;CC1=CC(=CC2=C1NC(=C3C(=CC=NC3=O)NC[C@H](C4=CC(=CC=C4)Cl)O)N2)N5CCOCC5	SCHEMBL13741284	Daporinad	STF-31	Narciclasine
GO_term
GO:0000012_1	-0.006564	-0.005680	0.003188	-0.005863	-0.003410	-0.002951	0.001118	0.002033	0.000799	-0.007842	...	-0.007256	-0.004271	-0.013783	-0.006253	0.002864	0.009604	-0.008099	-0.001475	-0.003698	-0.009866
GO:0000012_2	0.010029	0.011514	0.009892	0.012072	0.005788	0.012909	0.002316	0.009362	-0.011816	0.000166	...	0.008918	-0.002449	0.017704	0.006732	0.002447	0.006485	0.003888	-0.000569	0.001628	0.017132
GO:0000012_3	0.008466	0.006840	-0.000027	0.006379	0.003082	-0.006110	-0.008877	-0.000347	-0.013084	0.000150	...	-0.006096	0.011308	0.012216	0.000997	0.011521	0.013800	0.002843	0.016328	0.021640	0.003536
GO:0000012_4	0.013018	0.007276	0.010128	0.008622	0.004795	0.006706	0.000874	0.005514	-0.003347	-0.000010	...	-0.003682	0.006544	0.010806	0.003346	0.017556	0.023130	0.001105	0.009710	0.016940	0.014787
GO:0000012_5	-0.007076	-0.006129	-0.007634	-0.003785	-0.004151	-0.007947	-0.008430	-0.006039	-0.002722	0.002163	...	0.001821	-0.002346	-0.007831	-0.009368	-0.011118	-0.003408	-0.001760	0.003593	-0.000109	-0.020831

5 rows × 684 columns

ChEMBL Drug Target Slim

from chembl_webresource_client.new_client import new_client

Import SparseGO drugs

# Get names 
def get_compound_names(file_name):
    compounds = []

    with open(file_name, 'r') as fi:
        for line in fi:
            tokens = line.strip().split('\t')
            compounds.append(tokens[2].lower())

    return compounds
names = get_compound_names(computer+"SparseGO_code/data/compound_names.txt")
names.pop(0)

chEML IDs

Get chembl IDs of drugs if available (there are always 684 drugs, the compounds2ids object can be reused)

# Get all chembl IDs -- tarda
molecule = new_client.molecule

compounds2ids = {}
for i,drug in enumerate(names):
    
    if " + " in drug:
        drug_split = drug.split(" + ", 1)
        ID1 = list(molecule.filter(pref_name__iexact=drug_split[0]).only('molecule_chembl_id'))
        ID2 = list(molecule.filter(pref_name__iexact=drug_split[1]).only('molecule_chembl_id'))
        if len(ID1)>0 and len(ID2)>0:
            compounds2ids[drug]=[ID1[0]['molecule_chembl_id'],ID2[0]['molecule_chembl_id']]
        elif len(ID1)>0:
            compounds2ids[drug]=ID1[0]['molecule_chembl_id'] 
        elif len(ID2)>0:
            compounds2ids[drug]=ID2[0]['molecule_chembl_id'] 
        else:
            print(drug,i)

    else:
        ID = list(molecule.filter(pref_name__iexact=drug).only('molecule_chembl_id'))
        if len(ID)>0:
            ID = ID[0]['molecule_chembl_id']
            compounds2ids[drug]=ID
        else:
            # for drugs that have the chembl ID as the name!!
            ID = list(molecule.filter(chembl_id=drug).only('molecule_chembl_id')) 
            if len(ID)>0:
                ID = ID[0]['molecule_chembl_id']
                compounds2ids[drug]=ID
            else:
                # in case it is not found by pref_name
                ID = list(molecule.filter(molecule_synonyms__molecule_synonym__iexact=drug).only('molecule_chembl_id'))
                if len(ID)>0:
                    ID = ID[0]['molecule_chembl_id']
                    compounds2ids[drug]=ID
                else:
                    print(drug,i)
    # 341 chembl IDs where found (october 31 2022)

#manually add 6 more
compounds2ids["teniposide [usan]"]="CHEMBL452231"
compounds2ids["docetaxel (taxotere)"]="CHEMBL92"
compounds2ids["nan + navitoclax(1)"]="CHEMBL443684"
compounds2ids["nan + navitoclax(2)"]="CHEMBL443684"
compounds2ids["osi-027;coc1=cc=cc2=cc(=c3c4=c(n=cnn4c(=n3)c5ccc(cc5)c(=o)o)n)n=c21"]="CHEMBL3120215"
compounds2ids["paclitaxel;cc1=c2[c@h](c(=o)[c@@]3([c@h](c[c@@h]4[c@]([c@h]3[c@@h]([c@@](c2(c)c)(c[c@@h]1oc(=o)[c@@h]([c@h](c5=cc=cc=c5)nc(=o)c6=cc=cc=c6)o)o)oc(=o)c7=cc=cc=c7)(co4)oc(=o)c)o)c)oc(=o)c"]="CHEMBL428647"

len(compounds2ids)

347

chEMBL MoA (targets)

Get the molecule targets of each drug (if available)

compounds2targets = dict() # required to store the drug targets 
for drug in compounds2ids.keys():
    compounds2targets[drug] = set()

chembl_ids = list(compounds2ids.values()) # Chembl IDs of drugs

for drug in compounds2ids:
    # we jump from compounds to targets through activities:
    activities = new_client.mechanism.filter(parent_molecule_chembl_id__in=compounds2ids[drug]).only(
        ['parent_molecule_chembl_id', 'target_chembl_id'])
    # extracting target ChEMBL IDs from activities:
    for act in activities:
        compounds2targets[drug].add(act['target_chembl_id'])
    print(drug)
# We now know all targets for some drug

compounds2targets = {k: v for k, v in compounds2targets.items() if len(v) != 0 and len([x for x in list(v) if x is not None]) != 0 }
# 218 DRUGS HAVE ANNOTATED DRUG TARGETS

len(compounds2targets)

220

Drug slim GO terms

Get the GO terms of each target

# Get the GO terms of each target
compounds_GOterms = {}
for i in range(0, len(compounds2targets.keys())):
    compound = list(compounds2targets.keys())[i]
    GOterms_list = []
    
    for j in range(0, len(list(compounds2targets[compound]))):   
        target = list(compounds2targets[compound])[j]
        all_cross_references = list(new_client.target.filter(target_chembl_id=target).only(['target_components']).only(['target_components_xrefs']))[0]['target_components']
        if len(all_cross_references)>0: # not all targets have annotated go_terms
            for i in range(0, len(all_cross_references)):
                GOterms = all_cross_references[i]
                GOterms = pd.DataFrame(GOterms['target_component_xrefs'])
                GOterms = pd.concat([GOterms,pd.Series([target]).repeat(len(GOterms)).reset_index().pop(0)],axis=1) # add target ID to dataframe 
                GOterms_list= GOterms_list + GOterms.values.tolist()
    
    compounds_GOterms[compound] =  pd.DataFrame(GOterms_list).drop_duplicates()
    print(compound)

len(compounds_GOterms)

220

# we have 206 annotated drugs on CHEMBL

# add GO terms found in CTRPv2

CTRPv2_terms = pd.read_excel('ctrp_goterms_drugs.xlsx')  

# add GO terms of drugs with or without annotations
for drug in CTRPv2_terms["Drug"].unique():
    if drug not in list(compounds_GOterms.keys()): # some drugs had no previous data, no annotations from chembl
        compounds_GOterms[drug] = pd.DataFrame() # create empty dataframe

    for term in list(CTRPv2_terms.loc[CTRPv2_terms["Drug"]==drug]["Field"]):
        compounds_GOterms[drug] = pd.concat([compounds_GOterms[drug],pd.DataFrame([term,"","GoProcess",""]).transpose()])
    compounds_GOterms[drug] = compounds_GOterms[drug].drop_duplicates() 

# now we have 233 annotated drugs

# Delete drugs with no GOterms (some targets have no annotated GO terms)
compounds_GOterms = {k: v for k, v in compounds_GOterms.items() if len(v) != 0 } 

len(compounds_GOterms)

236

Match GO terms

Find all terms that match, terms that are part of both, the sparseGO graph and the drug slim results…

def load_ontology_extra_output(ontology_file, gene2id_mapping):
    """
    Creates the directed graph of the GO terms and stores the connected elements in arrays.

        Output
        ------
        dG: networkx.classes.digraph.DiGraph
            Directed graph of all terms

        terms_pairs: numpy.ndarray
            Store the connection between a term and a term

        genes_terms_pairs: numpy.ndarray
            Store the connection between a gene and a term
    """

    dG = nx.DiGraph() # Directed graph class

    file_handle = open(ontology_file) #  Open the file that has genes and go terms

    terms_pairs = [] # store the pairs between a term and a term
    genes_terms_pairs = [] # store the pairs between a gene and a term

    gene_set = set() # create a set (elements can't repeat)
    term_direct_gene_map = {}
    term_size_map = {}


    for line in file_handle:

        line = line.rstrip().split() # delete spaces and transform to list, line has 3 elements

        # No me hace falta el if, no tengo que separar las parejas
        if line[2] == 'default': # si el tercer elemento es default entonces se conectan los terms en el grafo
            dG.add_edge(line[0], line[1]) # Add an edge between line[0] and line[1]
            terms_pairs.append([line[0], line[1]]) # Add the pair to the list
        else:
            if line[1] not in gene2id_mapping: # se salta el gen si no es parte de los que estan en gene2id_mapping
                print(line[1])
                continue

            genes_terms_pairs.append([line[0], line[1]]) # add the pair

            if line[0] not in term_direct_gene_map: # si el termino todavia no esta en el diccionario lo agrega
                term_direct_gene_map[ line[0] ] = set() # crea un set

            term_direct_gene_map[line[0]].add(gene2id_mapping[line[1]]) # añadimos el gen al set de ese term

            gene_set.add(line[1]) # añadimos el gen al set total de genes

    terms_pairs = np.array(terms_pairs) # convert to 2d array
    genes_terms_pairs = np.array(genes_terms_pairs) # convert to 2d array

    file_handle.close()

    print('There are', len(gene_set), 'genes')

    for term in dG.nodes(): # hacemos esto para cada uno de los GO terms

        term_gene_set = set() # se crea un set

        if term in term_direct_gene_map:
            term_gene_set = term_direct_gene_map[term] # genes conectados al term

        deslist = nxadag.descendants(dG, term) #regresa todos sus GO terms descendientes (biological processes tiene 2085 descendientes, todos menos el mismo)

        for child in deslist:
            if child in term_direct_gene_map: # añadir los genes de sus descendientes
                term_gene_set = term_gene_set | term_direct_gene_map[child] # union of both sets, ahora tiene todos los genes los suyos y los de sus descendientes

        if len(term_gene_set) == 0:
            print('There is empty terms, please delete term:', term)
            sys.exit(1)
        else:
            # por ahora esta variable no me hace falta
            term_size_map[term] = len(term_gene_set) # cantidad de genes en ese term  (tomando en cuenta sus descendientes)

    leaves = [n for n in dG.nodes if dG.in_degree(n) == 0] # buscar la raiz
    #leaves = [n for n,d in dG.in_degree() if d==0]

    uG = dG.to_undirected() # Returns an undirected representation of the digraph
    connected_subG_list = list(nxacc.connected_components(uG)) #list of all GO terms

    # Verify my graph makes sense...
    print('There are', len(leaves), 'roots:', leaves[0])
    print('There are', len(dG.nodes()), 'terms')
    print('There are', len(connected_subG_list), 'connected components')
    if len(leaves) > 1:
        print('There are more than 1 root of ontology. Please use only one root.')
        sys.exit(1)
    if len(connected_subG_list) > 1:
        print( 'There are more than connected components. Please connect them.')
        sys.exit(1)

    return dG, terms_pairs, genes_terms_pairs, term_direct_gene_map, term_size_map

SparseGO graph

# Import SparseGO graph (to extract all nodes/terms)... 

# Load ontology: create the graph of connected GO terms
dG, terms_pairs, genes_terms_pairs, term_direct_gene_map, term_size_map = load_ontology_extra_output(onto, gene2id_mapping)
####
sparseGO_terms = list(dG.nodes())
sparseGO_terms.remove("GO:0008150")

There are 15015 genes
There are 1 roots: GO:0008150
There are 4184 terms
There are 1 connected components

Full GO graph

# Import full graph (to find parents)...
import obonet
#import networkx as nx
url = 'http://purl.obolibrary.org/obo/go/go-basic.obo'
full_graph = obonet.read_obo(url)
full_graph = full_graph.reverse() # change the direction of nodes
[n for n in full_graph.nodes if full_graph.in_degree(n) == 0] # graph contains the 3 roots (BP,MF,CC)

['GO:0003674', 'GO:0005575', 'GO:0008150']

Match terms!

Find all terms that match, terms that are part of both, the sparseGO graph and the drug slim results… if the slim terms’ ascendants are a match, they are also added

# Each model has DIFFERENT matches (the graph is different)
compounds_GOterms_matches = {}
for drug in compounds_GOterms.keys():
    # choose drug
    drug_df = compounds_GOterms[drug]
    drug_slim_GOterms = set(drug_df.loc[drug_df[2] == "GoProcess"][0]) # only GO processes
    #set(sparseGO_terms) & set(drug_slim_GOterms)
    drug_matches = [] # store all directly matched terms and matches with all parents
    for term in drug_slim_GOterms: # term ='GO:1902669' # buen ejemplo 
        
        if term in sparseGO_terms: # is the term in the sparseGO terms? 
            drug_matches.append([1,term]) # add to list
            #1: same term, 2:not direct match  (esto igual despues...the number indicates how direct is the relationship 0:same term, 1: parent, 2: grandpa, 3:...)
        
        # are its ascendants in the sparseGO terms? 
        parents = [source for source, _ in  full_graph.in_edges(term)] # parents of term
        relationship = 2
        while(len(parents)>0): # check all ascendants 
            #relationship+=1
            parents = [source for source, _ in  full_graph.in_edges(parents)] # parents of parents
        
            for parent_term in parents: # add parents that match sparseGO terms 
                if parent_term in sparseGO_terms:
                    drug_matches.append([relationship, parent_term])
        
        drug_matches = (pd.DataFrame(drug_matches).drop_duplicates()).values.tolist() # remove duplicates
        compounds_GOterms_matches[drug] = drug_matches
    print(drug)

# delete drugs that have no matches
compounds_GOterms_matches = {i:j for i,j in compounds_GOterms_matches.items() if j != []}

len(compounds_GOterms_matches)

230

SparseGO terms x drugSlim terms matrix

attribution_data_all.columns = attribution_data_all.columns.str.lower() # in order to match the term

attribution_data_all.head()

	brd-k02251932-001-01-3	brd-k25737009-001-01-2	nintedanib	bicalutamide	n-[(2r,3s)-2-[[cyclopropylmethyl(methyl)amino]methyl]-5-[(2r)-1-hydroxypropan-2-yl]-3-methyl-6-oxo-3,4-dihydro-2h-1,5-benzoxazocin-8-yl]-1-methyl-4-imidazolesulfonamide	pha-665752	n-cyclopropyl-3-[3-[[cyclopropyl(oxo)methyl]amino]-1h-indazol-6-yl]benzamide	ki8751	ipa-3	fawugygebhaqbu-ppexnqrjsa-n	...	ml031	semagacestat	rita	cdk9 inhibitor	dasatinib	bms-536924;cc1=cc(=cc2=c1nc(=c3c(=cc=nc3=o)nc[c@h](c4=cc(=cc=c4)cl)o)n2)n5ccocc5	schembl13741284	daporinad	stf-31	narciclasine
GO_term
GO:0000012_1	-0.006564	-0.005680	0.003188	-0.005863	-0.003410	-0.002951	0.001118	0.002033	0.000799	-0.007842	...	-0.007256	-0.004271	-0.013783	-0.006253	0.002864	0.009604	-0.008099	-0.001475	-0.003698	-0.009866
GO:0000012_2	0.010029	0.011514	0.009892	0.012072	0.005788	0.012909	0.002316	0.009362	-0.011816	0.000166	...	0.008918	-0.002449	0.017704	0.006732	0.002447	0.006485	0.003888	-0.000569	0.001628	0.017132
GO:0000012_3	0.008466	0.006840	-0.000027	0.006379	0.003082	-0.006110	-0.008877	-0.000347	-0.013084	0.000150	...	-0.006096	0.011308	0.012216	0.000997	0.011521	0.013800	0.002843	0.016328	0.021640	0.003536
GO:0000012_4	0.013018	0.007276	0.010128	0.008622	0.004795	0.006706	0.000874	0.005514	-0.003347	-0.000010	...	-0.003682	0.006544	0.010806	0.003346	0.017556	0.023130	0.001105	0.009710	0.016940	0.014787
GO:0000012_5	-0.007076	-0.006129	-0.007634	-0.003785	-0.004151	-0.007947	-0.008430	-0.006039	-0.002722	0.002163	...	0.001821	-0.002346	-0.007831	-0.009368	-0.011118	-0.003408	-0.001760	0.003593	-0.000109	-0.020831

5 rows × 684 columns

attribution_data_all.shape

(25098, 684)

Only keep drugs that have annotated GO terms

attribution_data_annotated = attribution_data_all[list(compounds_GOterms_matches.keys())]

attribution_data_annotated.shape # 230 DRUGS

(25098, 230)

Build drugSlim (MoA) matrix

slim_matrix = attribution_data_annotated.copy() # copy dataframe in order to build a similar matrix 
for col in slim_matrix.columns:
    slim_matrix[col].values[:] = 0 # empty matrix 

for drug in compounds_GOterms_matches.keys():
    drug_matches = compounds_GOterms_matches[drug]
    drug_matches_names = list(pd.DataFrame(drug_matches)[1])
    
    drug_matches_names_duplicated = []
    for term in set(drug_matches_names):
        for i in range(1,7):
            drug_matches_names_duplicated.append(term+"_"+str(i))
    
    slim_matrix[drug][drug_matches_names_duplicated] = 1 # add a 1 if term is annotated to drug

SVM

from sklearn.model_selection import train_test_split
from sklearn import metrics
from sklearn.linear_model import LogisticRegression

from sklearn import svm

slim_matrix_single_neuron = pd.DataFrame(0, index=sparseGO_terms, columns=slim_matrix.columns)
preds_svm_matrix = pd.DataFrame(0, index=sparseGO_terms, columns=slim_matrix.columns)
platt_matrix = pd.DataFrame(0, index=sparseGO_terms, columns=slim_matrix.columns)
distance_matrix = pd.DataFrame(0, index=sparseGO_terms, columns=slim_matrix.columns)
delta_logits_matrix  = pd.DataFrame(0, index=sparseGO_terms, columns=slim_matrix.columns)

Create models

Regression models…

# Dictionaries to store results
GO_terms_auc_svm = {}
GO_terms_aupr_svm = {}
GO_terms_precision_svm = {}
GO_terms_auc_delta_logits = {}

# Perform logistic
for goterm in sparseGO_terms:
    # if (real_go_info[real_go_info["GO_term"]==goterm+"_1"]["layer_number"]).values >3:
    #     continue
    
    # store results of each cross validation
    all_y_test = []
    all_y_pred_proba = []
    all_y_pred_proba_dis = []
    all_y_pred = []
    all_y_names = []

    goterm_drugs = slim_matrix.loc[[goterm+"_"+str(1)]].values.flatten()

    if sum(goterm_drugs) <= 8: # at least 2 annotated drugs in each group
            continue

    list_nodes = []
    for i in range(1,7):
        list_nodes.append(goterm+"_"+str(i))

    score = attribution_data_annotated.loc[list_nodes].T
    #score_mod = score
    score_mod = score.divide(score.std()).fillna(0) # AFECTA MUCHO

    # Separate drugs in 4 groups for cross-validation -----

    # Split data in 2 groups (with train_test_split in order to have 0s in both groups)
    X_part1,X_part2,y_part1,y_part2=train_test_split(score_mod,goterm_drugs,test_size=0.50,random_state=0,stratify=goterm_drugs)
    # Split data again in 4 groups (split data previously split)
    X_group1,X_group2,y_group1,y_group2=train_test_split(X_part1,y_part1,test_size=0.50,random_state=0,stratify=y_part1)
    X_group3,X_group4,y_group3,y_group4=train_test_split(X_part2,y_part2,test_size=0.50,random_state=0,stratify=y_part2)

    for i in  range(1,5):
        vector = range(0,5)
        group_number = str(i)
        X_test = globals()["X_group"+group_number]
        y_test = globals()["y_group"+group_number]

        # Use the other 3 groups for training 
        keep = list({1,2,3,4}-{int(group_number)}) # remove group number of current test 
        X_train = pd.concat((globals()["X_group"+str(keep[0])],globals()["X_group"+str(keep[1])],globals()["X_group"+str(keep[2])]))
        y_train = np.concatenate((globals()["y_group"+str(keep[0])],globals()["y_group"+str(keep[1])],globals()["y_group"+str(keep[2])]))
        
        #gamma = 1/(X_train.shape[1]*X_train.to_numpy().var())
        gamma = "scale"
        C=1
        
        svm_model = svm.SVC(C=C,gamma=gamma, kernel='rbf',
                           class_weight="balanced",
                            tol=0.001,
                            probability=True,
                            random_state=1234)
        # svm_model = svm.SVC(gamma='auto', kernel='rbf',class_weight="balanced",probability=True)
        
        # fit the model with data
        svm_model.fit(X_train,y_train)
        y_pred=svm_model.predict(X_test)
        y_pred_proba = svm_model.predict_proba(X_test)[::,1] # platt values
        y_pred_proba_dis = svm_model.decision_function(X_test) # An SVM returns a real-valued prediction for each of the input data samples, which corresponds to its distance from the separating hyperplane.
        #  decision_function SORTS the results from most probable class to the least probable one.
        
        all_y_test.append(y_test)
        all_y_pred_proba.append(y_pred_proba)
        all_y_pred_proba_dis.append(y_pred_proba_dis)
        all_y_pred.append(y_pred)
        all_y_names.append(X_test.index)

    all_y_test = np.concatenate(all_y_test)
    all_y_pred_proba = np.concatenate(all_y_pred_proba)
    all_y_pred_proba_dis = np.concatenate(all_y_pred_proba_dis)
    all_y_names = np.concatenate(all_y_names)
    all_y_pred = np.concatenate(all_y_pred)
    
    percentage_go_annotations = sum(all_y_test)/len(all_y_test)
    logits_apriori=np.log(percentage_go_annotations/(1-percentage_go_annotations))
    logits_apost= np.log(all_y_pred_proba/(1-all_y_pred_proba))
    delta_logits = logits_apost-logits_apriori

    platt_matrix.loc[goterm,all_y_names] = all_y_pred_proba
    distance_matrix.loc[goterm,all_y_names] = all_y_pred_proba_dis
    slim_matrix_single_neuron.loc[goterm,all_y_names] = all_y_test
    preds_svm_matrix.loc[goterm,all_y_names] = all_y_pred
    
    delta_logits_matrix.loc[goterm,all_y_names] = delta_logits

    GO_terms_auc_delta_logits[goterm] = metrics.roc_auc_score(all_y_test, delta_logits)
    GO_terms_auc_svm[goterm] = metrics.roc_auc_score(all_y_test, all_y_pred_proba)

    precision, recall, thresholds = metrics.precision_recall_curve(all_y_test, all_y_pred_proba)
    GO_terms_aupr_svm[goterm] = metrics.auc(recall, precision)
    GO_terms_precision_svm[goterm] = metrics.precision_score(all_y_test, all_y_pred)

# done with platt values
GO_terms_auc_svm_df = pd.DataFrame(list(GO_terms_auc_svm.items()),columns = ['goterm','auc']).set_index("goterm")
GO_terms_auc_svm_df = GO_terms_auc_svm_df.dropna()
GO_terms_auc_svm_df.sort_values(by=["auc"], ascending=False).head()

	auc
goterm
GO:0036289	0.999708
GO:0060440	0.994743
GO:0042149	0.971292
GO:1902455	0.969545
GO:0001556	0.965979

print("There are " +str(len(GO_terms_auc_svm_df))+ " svm models.")

There are 939 svm models.

# only keep goterms that have a model 
platt_matrix = platt_matrix.loc[list(GO_terms_auc_svm_df.index),:]
distance_matrix = distance_matrix.loc[list(GO_terms_auc_svm_df.index),:]
slim_matrix_single_neuron  = slim_matrix_single_neuron.loc[list(GO_terms_auc_svm_df.index),:]
preds_svm_matrix  = preds_svm_matrix.loc[list(GO_terms_auc_svm_df.index),:]
delta_logits_matrix  = delta_logits_matrix.loc[list(GO_terms_auc_svm_df.index),:]

AUC histogram

sns.set(rc={'figure.figsize':(10,6)})
fig, ax = plt.subplots()
perc = str(round((100*len(GO_terms_auc_svm_df[GO_terms_auc_svm_df["auc"]>0.69])/len(GO_terms_auc_svm_df)),2))+"%"
N, bins, patches = plt.hist(GO_terms_auc_svm_df, color=CB_color_cycle[6],bins=50, linewidth=0.1)

for i in range(0,len(bins)-1):
    if bins[i]>0.69:
        patches[i].set_facecolor(CB_color_cycle[2])

plt.yticks(fontsize=16)
plt.xticks(fontsize=16)

ax.spines['top'].set_visible(False)
ax.spines['right'].set_visible(False)
ax.spines['left'].set_visible(False)
ax.spines['bottom'].set_color('#DDDDDD')

# Second, remove the ticks as well.
ax.tick_params(bottom=False, left=True)

# Third, add a horizontal grid (but keep the vertical grid hidden).
# Color the lines a light gray as well.
ax.set_axisbelow(True)
ax.yaxis.grid(True, color='#EEEEEE')
ax.xaxis.grid(False)

plt.xlabel("AUC value", fontsize=20)
plt.ylabel("Number of GO term models", fontsize=20)
colors2 = {'GO term models with AUC>=0.7':CB_color_cycle[2]}  
labels = list(colors2.keys())
handles = [plt.Rectangle((0,0),1,1, color=colors2[label]) for label in labels]
plt.legend(handles, labels,fontsize=20, loc="lower left", bbox_to_anchor=(0.35,-0.35))
plt.text(0.71, 8, str(perc), fontsize=20,color='#333333')
plt.title("Overall performance of the models using expression", fontsize=24)
# con el que mejor funciona es con la suma normal del attribution 
fig.tight_layout()
fig.savefig(resultsdir+'modelsAUCsvm.png', transparent=True)

AUC waterfall plot

GO_terms_auc_svm_df =GO_terms_auc_svm_df.sort_values(by=["auc"], ascending=False)

plt.rcParams['figure.figsize'] = (12, 9)
drugs = GO_terms_auc_svm_df.index
rhos = GO_terms_auc_svm_df["auc"]

percentage = round((sum(rhos>0.69)/len(rhos))*100,1)

fig, ax = plt.subplots()
#colors = ['#208EA3' if (x < 0.5) else '#A4C61A' for x in rhos ]
colors = ['#C9C9C9' if (x < 0.69) else "#6492CA" for x in rhos ]
ax.bar(
    x=drugs,
    height=rhos,
    edgecolor=colors,
    linewidth=2
)
plt.xticks([])
plt.yticks(fontsize=28)


# First, let's remove the top, right and left spines (figure borders)
# which really aren't necessary for a bar chart.
# Also, make the bottom spine gray instead of black.
ax.spines['top'].set_visible(False)
ax.spines['right'].set_visible(False)
ax.spines['left'].set_visible(False)
ax.spines['bottom'].set_visible(False)
#ax.spines['bottom'].set_color('#DDDDDD')

# Second, remove the ticks as well.
ax.tick_params(bottom=False, left=False)

# Third, add a horizontal grid (but keep the vertical grid hidden).
# Color the lines a light gray as well.
ax.set_axisbelow(False)
ax.yaxis.grid(False)
#ax.yaxis.grid(True, color='#EEEEEE')
ax.xaxis.grid(False)


# Add labels and a title. Note the use of `labelpad` and `pad` to add some
# extra space between the text and the tick labels.
ax.set_xlabel('SVM models', labelpad=-30, color='#333333',fontsize=50)
ax.set_ylabel('AUC-ROC value', labelpad=15, color='#333333',fontsize=50)
ax.set_title('', color='#333333',
             weight='bold')

colors2 = {'High confidence drugs (r>0.5)':'#A4C61A'}  
labels = list(colors2.keys())
handles = [plt.Rectangle((0,0),1,1, color=colors2[label]) for label in labels]
#plt.legend(handles, labels,fontsize=40, loc="lower left",bbox_to_anchor=(0, -0.215))
plt.text(77, 0.32, str(percentage)+"%", fontsize=60,color='#000000')

plt.ylim((-0.1,1.1))
# Make the chart fill out the figure better.
fig.tight_layout()
fig.savefig(resultsdir+'WaterfallModelsSVM.png', transparent=True)

AUC boxplot by parents

# Add number of parents
number_parents = {}
levels = {}
for i in range(0,len(GO_terms_auc_svm_df.index)):
    term = GO_terms_auc_svm_df.index[i]
    number_parents[GO_terms_auc_svm_df.index[i]]=len([source for source, _ in  dG.in_edges(term)])
    levels[GO_terms_auc_svm_df.index[i]]=level_number[term]-1
levels = pd.DataFrame.from_dict(levels, orient='index')
number_parents = pd.DataFrame.from_dict(number_parents, orient='index')

GO_terms_auc_svm_df = pd.concat([GO_terms_auc_svm_df, levels,number_parents], axis=1)
GO_terms_auc_svm_df.columns = ["auc","levels","parents"]

GO_terms_auc_svm_df.head()

	auc	levels	parents
GO:0000077	0.284021	1	3
GO:0045737	0.835954	0	8
GO:0000082	0.732331	2	4
GO:1900087	0.593301	0	10
GO:2000134	0.865329	1	9

import plotly.express as px

c = ['#E8384F', '#FD817D', '#FDAE33',
         '#EECC16', '#A4C61A', '#37A862',"#208EA3","#3B6EAB"]

df = px.data.tips()
fig = px.box(GO_terms_auc_svm_df, x="levels", y="auc",
             color="levels",
            color_discrete_sequence=c,
             width =600,
             height=400,
              template="simple_white",
              labels=dict(levels="Level of GO hierarchy", auc="AUC-ROC")
            )
fig.update_traces(width=0.9)

fig.add_shape( # add a horizontal "target" line
    type="line", line_color="salmon", line_width=3, opacity=1, line_dash="dot",
    x0=0, x1=1, xref="paper", y0=0.7, y1=0.7, yref="y"
)


fig.update_layout(
   title=dict(text="<b> AUC value grouped by level of GO hierarchy <b>",
             x=0.5,
             y=0.9,
              font=dict(size=18),
              xanchor='center',
              yanchor='top'),
    xaxis=dict(ticks="", showticklabels=False, showgrid=False, zeroline=False),
    yaxis=dict(ticks="", showticklabels=True, showgrid=True, zeroline=False),
 #   yaxis_range=[min(yy.flatten()),max(yy.flatten())],
  #  xaxis_range=[min(xx.flatten()),max(xx.flatten())],
    legend=dict(x=1.1, y=1, orientation="v",font=dict(size=11)),
    paper_bgcolor='rgba(0,0,0,0)',
    font=dict(family='Roboto',color= "#36382E",size=15)
    )

fig.show()

TOP 15 PREDICTED GO TERMS

top15goterms= np.array(GO_terms_auc_svm_df.sort_values(by=["auc"], ascending=False)[0:15].index)

Get Top GO term names

top15goterms_1 = []
for goterm in top15goterms:
    top15goterms_1.append(goterm+"_"+str(1))
real_go_info_mod_best = real_go_info[real_go_info.GO_term.isin(top15goterms_1)]
real_go_info_mod_best.GO_term = real_go_info_mod_best.GO_term.str.replace("_1","")

top15goterms_auc = GO_terms_auc_svm_df.sort_values(by=["auc"], ascending=False)[0:15].reset_index()
top15goterms_auc.columns=["GO_term","auc","levels","parents"]

top15goterms_auc = top15goterms_auc.merge(real_go_info_mod_best[real_go_info_mod_best["GO_term"].isin(top15goterms)], on="GO_term")

top15goterms_auc

	GO_term	auc	levels	parents	Name	layer_number
0	GO:0036289	0.999708	0	2	Peptidyl-serine autophosphorylation (1)	0.0
1	GO:0060440	0.994743	0	4	Trachea formation (1)	0.0
2	GO:0042149	0.971292	0	1	Cellular response to glucose starvation (1)	0.0
3	GO:1902455	0.969545	0	2	Negative regulation of stem cell population maintenance (1)	0.0
4	GO:0001556	0.965979	0	6	Oocyte maturation (1)	0.0
5	GO:0045636	0.955115	0	6	Positive regulation of melanocyte differentiation (1)	0.0
6	GO:0010750	0.955000	0	4	Positive regulation of nitric oxide mediated signal transduction (1)	0.0
7	GO:0060020	0.949434	0	1	Bergmann glial cell differentiation (1)	0.0
8	GO:1902042	0.945804	0	4	Negative regulation of extrinsic apoptotic signaling pathway via death domain receptors (1)	0.0
9	GO:1902236	0.941667	0	12	Negative regulation of endoplasmic reticulum stress-induced intrinsic apoptotic signaling pathway (1)	0.0
10	GO:0070059	0.936432	1	2	Intrinsic apoptotic signaling pathway in response to endoplasmic reticulum stress (1)	1.0
11	GO:0051453	0.935521	1	2	Regulation of intracellular ph (1)	1.0
12	GO:0042659	0.931364	0	3	Regulation of cell fate specification (1)	0.0
13	GO:0006360	0.930046	2	7	Transcription by rna polymerase i (1)	2.0
14	GO:0006959	0.921730	2	2	Humoral immune response (1)	2.0

WORST 15 PREDICTED GO TERMS

worst15goterms= np.array(GO_terms_auc_svm_df.sort_values(by=["auc"], ascending=True)[0:15].index)

Get Worst GO term names

worst15goterms_1 = []
for goterm in worst15goterms:
    worst15goterms_1.append(goterm+"_"+str(1))
real_go_info_mod_worst = real_go_info[real_go_info.GO_term.isin(worst15goterms_1)]
real_go_info_mod_worst.GO_term = real_go_info_mod_worst.GO_term.str.replace("_1","")

worst15goterms_auc = GO_terms_auc_svm_df.sort_values(by=["auc"], ascending=True)[0:15].reset_index()
worst15goterms_auc.columns=["GO_term","auc","levels","parents"]

worst15goterms_auc.merge(real_go_info_mod_worst[real_go_info_mod_worst["GO_term"].isin(worst15goterms)], on="GO_term")

	GO_term	auc	levels	parents	Name	layer_number
0	GO:0000077	0.284021	1	3	Dna damage checkpoint signaling (1)	1.0
1	GO:0006869	0.299648	3	2	Lipid transport (1)	3.0
2	GO:0051302	0.314545	1	2	Regulation of cell division (1)	1.0
3	GO:0016485	0.318636	3	5	Protein processing (1)	3.0
4	GO:0019722	0.322272	2	1	Calcium-mediated signaling (1)	2.0
5	GO:0046854	0.326276	1	2	Phosphatidylinositol phosphate biosynthetic process (1)	1.0
6	GO:0060740	0.331825	1	6	Prostate gland epithelium morphogenesis (1)	1.0
7	GO:0060444	0.347273	1	8	Branching involved in mammary gland duct morphogenesis (1)	1.0
8	GO:0006919	0.352725	1	3	Activation of cysteine-type endopeptidase activity involved in apoptotic process (1)	1.0
9	GO:0032436	0.353421	1	14	Positive regulation of proteasomal ubiquitin-dependent protein catabolic process (1)	1.0
10	GO:0055119	0.353947	1	1	Relaxation of cardiac muscle (1)	1.0
11	GO:0001892	0.355979	1	5	Embryonic placenta development (1)	1.0
12	GO:0031295	0.364518	0	8	T cell costimulation (1)	0.0
13	GO:0046620	0.365476	1	3	Regulation of organ growth (1)	1.0
14	GO:0008361	0.367423	2	1	Regulation of cell size (1)	2.0

AUPR histogram

GO_terms_aupr_svm_df = pd.DataFrame(list(GO_terms_aupr_svm.items()),columns = ['goterm','aupr']).set_index("goterm")
GO_terms_aupr_svm_df = GO_terms_aupr_svm_df.dropna()
GO_terms_aupr_svm_df.sort_values(by=["aupr"], ascending=False).head()

	aupr
goterm
GO:0036289	0.996209
GO:0006807	0.945077
GO:0050896	0.921869
GO:0043170	0.909722
GO:0009058	0.900903

# Add number of parents
number_parents = {}
levels = {}
for i in range(0,len(GO_terms_aupr_svm_df.index)):
    term = GO_terms_aupr_svm_df.index[i]
    number_parents[GO_terms_aupr_svm_df.index[i]]=len([source for source, _ in  dG.in_edges(term)])
    levels[GO_terms_aupr_svm_df.index[i]]=level_number[term]-1
levels = pd.DataFrame.from_dict(levels, orient='index')
number_parents = pd.DataFrame.from_dict(number_parents, orient='index')

GO_terms_aupr_svm_df = pd.concat([GO_terms_aupr_svm_df, levels,number_parents], axis=1)
GO_terms_aupr_svm_df.columns = ["aupr","levels","parents"]

c = ['#E8384F', '#FD817D', '#FDAE33',
         '#EECC16', '#A4C61A', '#37A862',"#208EA3","#3B6EAB"]

df = px.data.tips()
fig = px.box(GO_terms_aupr_svm_df, x="levels", y="aupr",
             color="levels",
            color_discrete_sequence=c,
             width =600,
             height=400,
              template="simple_white",
              labels=dict(levels="Level of GO hierarchy", aupr="AUPR")
            )
fig.update_traces(width=0.9)

fig.add_shape( # add a horizontal "target" line
    type="line", line_color="salmon", line_width=3, opacity=1, line_dash="dot",
    x0=0, x1=1, xref="paper", y0=0.7, y1=0.7, yref="y"
)


fig.update_layout(
   title=dict(text="<b> AUPR value grouped by level of GO hierarchy <b>",
             x=0.5,
             y=0.9,
              font=dict(size=18),
              xanchor='center',
              yanchor='top'),
    xaxis=dict(ticks="", showticklabels=False, showgrid=False, zeroline=False),
    yaxis=dict(ticks="", showticklabels=True, showgrid=True, zeroline=False),
 #   yaxis_range=[min(yy.flatten()),max(yy.flatten())],
  #  xaxis_range=[min(xx.flatten()),max(xx.flatten())],
    legend=dict(x=1.1, y=1, orientation="v",font=dict(size=11)),
    paper_bgcolor='rgba(0,0,0,0)',
    font=dict(family='Roboto',color= "#36382E",size=15)
    )

fig.show()
pio.write_image(fig, resultsdir+"AUPR_levels.png", width=600, height=400,scale=8)

Example prediction

def f2(goterm):    
    return goterm

combobox_go = interactive(f2, goterm=widgets.Combobox(options=list(GO_terms_auc_svm_df.sort_values(by=["auc"], ascending=False).index)))

Choose drug to study…

display(combobox_go)

selected_go = combobox_go.result

#auc
plt.rcParams['figure.figsize'] = (4, 2)
fpr, tpr, _ = metrics.roc_curve(slim_matrix_single_neuron.loc[selected_go],  platt_matrix.loc[selected_go])
auc = metrics.roc_auc_score(slim_matrix_single_neuron.loc[selected_go],  platt_matrix.loc[selected_go])
plt.plot(fpr,tpr,label="data 1, auc="+str(auc))
plt.legend(loc=4)
plt.show()

plot = pd.concat([pd.DataFrame(slim_matrix_single_neuron.loc[selected_go]),pd.DataFrame(platt_matrix.loc[selected_go])], axis=1)
plot.columns = ["slim","probability"]
ax = sns.boxplot(x="slim", y="probability", data=plot,showfliers=False )

#auc 
fpr, tpr, _ = metrics.roc_curve(slim_matrix_single_neuron.loc[selected_go],  delta_logits_matrix.loc[selected_go])
auc = metrics.roc_auc_score(slim_matrix_single_neuron.loc[selected_go],  delta_logits_matrix.loc[selected_go])
plt.plot(fpr,tpr,label="data 1, auc="+str(auc))
plt.legend(loc=4)
plt.show()

plot = pd.concat([pd.DataFrame(slim_matrix_single_neuron.loc[selected_go]),pd.DataFrame(delta_logits_matrix.loc[selected_go])], axis=1)
plot.columns = ["slim","probability"]
ax = sns.boxplot(x="slim", y="probability", data=plot,showfliers=False )

plt.rcParams['figure.figsize'] = (2, 2)
metrics.ConfusionMatrixDisplay.from_predictions(slim_matrix_single_neuron.loc[selected_go], preds_svm_matrix.loc[selected_go])
plt.grid(visible=None)

print("Accuracy:",metrics.accuracy_score(slim_matrix_single_neuron.loc[selected_go], preds_svm_matrix.loc[selected_go]))
print("Precision:",metrics.precision_score(slim_matrix_single_neuron.loc[selected_go], preds_svm_matrix.loc[selected_go]))
print("Recall:",metrics.recall_score(slim_matrix_single_neuron.loc[selected_go], preds_svm_matrix.loc[selected_go])) #TP / (TP+FN)
print("AUC with score:",auc) #TP / (TP+FN)

Accuracy: 0.9782608695652174
Precision: 0.7894736842105263
Recall: 0.9375
AUC with score: 0.9947429906542057

TN - FP

FN - TP

plt.rcParams['figure.figsize'] = (4, 2)
precision, recall, thresholds = metrics.precision_recall_curve(slim_matrix_single_neuron.loc[selected_go],  preds_svm_matrix.loc[selected_go])
auc_precision_recall = metrics.auc(recall, precision)
plt.plot(recall, precision,label=str(auc_precision_recall))
plt.legend(loc=4)
plt.show()

METRICS drugs

auc_drugs = {}
aupr_drugs = {}
precision_drugs = {}
for drug in list(slim_matrix_single_neuron.columns):
    if slim_matrix_single_neuron.loc[:,drug].sum() ==0:
        continue
    #fpr, tpr, _ = metrics.roc_curve(slim_matrix_single_neuron.loc[:,drug], logits_matrix.loc[:,drug])
    #auc_drugs[drug]  = metrics.auc(fpr, tpr)
    auc_drugs[drug] = metrics.roc_auc_score(slim_matrix_single_neuron.loc[:,drug],  platt_matrix.loc[:,drug])
    precision, recall, thresholds = metrics.precision_recall_curve(slim_matrix_single_neuron.loc[:,drug],  platt_matrix.loc[:,drug])
    aupr_drugs[drug] = metrics.auc(recall, precision)
    precision_drugs[drug] = metrics.precision_score(slim_matrix_single_neuron.loc[:,drug],  preds_svm_matrix.loc[:,drug])

auc_drugs_df = pd.DataFrame(list(auc_drugs.items()),columns = ['goterm','auc']).set_index("goterm")
auc_drugs_df = auc_drugs_df.dropna()

aupr_drugs_df = pd.DataFrame(list(aupr_drugs.items()),columns = ['goterm','aupr']).set_index("goterm")
aupr_drugs_df = aupr_drugs_df.dropna()

precision_drugs_df = pd.DataFrame(list(precision_drugs.items()),columns = ['goterm','precision']).set_index("goterm")
precision_drugs_df = precision_drugs_df.dropna()

AUC histogram drugs

sns.set(rc={'figure.figsize':(10,6)})
fig, ax = plt.subplots()
perc = str(round((100*len(auc_drugs_df[auc_drugs_df["auc"]>0.7])/len(auc_drugs_df)),2))+"%"
N, bins, patches = plt.hist(auc_drugs_df, color=CB_color_cycle[6],bins=50, linewidth=0.1)

for i in range(0,len(bins)-1):
    if bins[i]>0.7:
        patches[i].set_facecolor(CB_color_cycle[5])

plt.yticks(fontsize=16)
plt.xticks(fontsize=16)

ax.spines['top'].set_visible(False)
ax.spines['right'].set_visible(False)
ax.spines['left'].set_visible(False)
ax.spines['bottom'].set_color('#DDDDDD')

# Second, remove the ticks as well.
ax.tick_params(bottom=False, left=True)

# Third, add a horizontal grid (but keep the vertical grid hidden).
# Color the lines a light gray as well.
ax.set_axisbelow(True)
ax.yaxis.grid(True, color='#EEEEEE')
ax.xaxis.grid(False)

plt.xlabel("AUC value", fontsize=20)
plt.ylabel("Number of drugs", fontsize=20)
colors2 = {'Drugs with AUC>=0.7':CB_color_cycle[5]}  
labels = list(colors2.keys())
handles = [plt.Rectangle((0,0),1,1, color=colors2[label]) for label in labels]
plt.legend(handles, labels,fontsize=20, loc="lower left", bbox_to_anchor=(0.35,-0.35))
plt.text(0.79, 6, str(perc), fontsize=20,color='#333333')
plt.title("Overall performance by drugs using mutations", fontsize=24)
# con el que mejor funciona es con la suma normal del attribution 
fig.tight_layout()
fig.savefig(resultsdir+'drugsAUC.png', transparent=True)

AUC waterfall plot drugs

auc_drugs_df =auc_drugs_df.sort_values(by=["auc"], ascending=False)

plt.rcParams['figure.figsize'] = (12, 9)
drugs = auc_drugs_df.index
rhos = auc_drugs_df["auc"]

percentage = round((sum(rhos>0.69)/len(rhos))*100,1)

fig, ax = plt.subplots()
#colors = ['#208EA3' if (x < 0.5) else '#A4C61A' for x in rhos ]
colors = ['#C9C9C9' if (x < 0.69) else "#B678BE" for x in rhos ]
ax.bar(
    x=drugs,
    height=rhos,
    edgecolor=colors,
    linewidth=3
)
plt.xticks([])
plt.yticks(fontsize=28)


# First, let's remove the top, right and left spines (figure borders)
# which really aren't necessary for a bar chart.
# Also, make the bottom spine gray instead of black.
ax.spines['top'].set_visible(False)
ax.spines['right'].set_visible(False)
ax.spines['left'].set_visible(False)
ax.spines['bottom'].set_visible(False)
#ax.spines['bottom'].set_color('#DDDDDD')

# Second, remove the ticks as well.
ax.tick_params(bottom=False, left=False)

# Third, add a horizontal grid (but keep the vertical grid hidden).
# Color the lines a light gray as well.
ax.set_axisbelow(False)
ax.yaxis.grid(False)
#ax.yaxis.grid(True, color='#EEEEEE')
ax.xaxis.grid(False)


# Add labels and a title. Note the use of `labelpad` and `pad` to add some
# extra space between the text and the tick labels.
ax.set_xlabel('Drugs', labelpad=-30, color='#333333',fontsize=50)
ax.set_ylabel('AUC-ROC value', labelpad=15, color='#333333',fontsize=50)
ax.set_title('', color='#333333',
             weight='bold')

colors2 = {'High confidence drugs (r>0.5)':'#A4C61A'}  
labels = list(colors2.keys())
handles = [plt.Rectangle((0,0),1,1, color=colors2[label]) for label in labels]
#plt.legend(handles, labels,fontsize=40, loc="lower left",bbox_to_anchor=(0, -0.215))
plt.text(77, 0.32, str(percentage)+"%", fontsize=60,color='#000000')

plt.ylim((-0.1,1.1))
# Make the chart fill out the figure better.
fig.tight_layout()
fig.savefig(resultsdir+'WaterfallModelsSVM_drugs.png', transparent=True)

AUPR histogram drugs

sns.set(rc={'figure.figsize':(5,3)})
perc = str(round((100*len(aupr_drugs_df[aupr_drugs_df["aupr"]>0.69])/len(aupr_drugs_df)),2))+"%"
N, bins, patches = plt.hist(aupr_drugs_df, color=CB_color_cycle[6],bins=50, linewidth=0.1)
for i in range(0,len(bins)-1):
    if bins[i]>0.69:
        patches[i].set_facecolor(CB_color_cycle[3])

plt.xlabel("AUPR drugs", fontsize=16)  
plt.title(perc, fontsize=16)

Text(0.5, 1.0, '33.62%')

Example drug prediction

def f(drug):    
    
    return drug

predictions_nodes = []
for goterm in list(platt_matrix.index):
    predictions_nodes.append(goterm+"_"+str(1))

# add names to go terms
real_go_info_svm= real_go_info[real_go_info.GO_term.isin(predictions_nodes)]
real_go_info_svm.GO_term = real_go_info_svm.GO_term.str.replace("_1","")

combobox = interactive(f, drug=widgets.Combobox(options=list(precision_drugs_df.sort_values(by=["precision"], ascending=False).index)))

Choose drug to study…

display(combobox)

selected_drug_name = combobox.result

sns.set(rc={'figure.figsize':(4,2)})
#auc
fpr, tpr, _ = metrics.roc_curve(slim_matrix_single_neuron.loc[:,selected_drug_name], platt_matrix.loc[:,selected_drug_name] )
auc = metrics.roc_auc_score(slim_matrix_single_neuron.loc[:,selected_drug_name],  platt_matrix.loc[:,selected_drug_name])
plt.plot(fpr,tpr,label="data 1, auc="+str(auc))
plt.legend(loc=4)
plt.show()

plot = pd.concat([pd.DataFrame(slim_matrix_single_neuron.loc[:,selected_drug_name]),pd.DataFrame(platt_matrix.loc[:,selected_drug_name])], axis=1)
plot.columns = ["slim","svm score"]
ax = sns.boxplot(x="slim", y="svm score", data=plot,showfliers=False )

plot = pd.concat([pd.DataFrame(slim_matrix.loc[:,selected_drug_name]),pd.DataFrame(attribution_data_annotated.loc[:,selected_drug_name]*1e4)], axis=1)
plot.columns = ["slim","attribution"]
ax = sns.boxplot(x="slim", y="attribution", data=plot,showfliers=True )

metrics.ConfusionMatrixDisplay.from_predictions(slim_matrix_single_neuron.loc[:,selected_drug_name].round(), preds_svm_matrix.loc[:,selected_drug_name])

<sklearn.metrics._plot.confusion_matrix.ConfusionMatrixDisplay at 0x2502863c8b0>

print("Accuracy:",metrics.accuracy_score(slim_matrix_single_neuron.loc[:,selected_drug_name], preds_svm_matrix.loc[:,selected_drug_name]))
print("Precision:",metrics.precision_score(slim_matrix_single_neuron.loc[:,selected_drug_name], preds_svm_matrix.loc[:,selected_drug_name]))
print("Recall:",metrics.recall_score(slim_matrix_single_neuron.loc[:,selected_drug_name], preds_svm_matrix.loc[:,selected_drug_name])) #TP / (TP+FN)
print("AUC with score:",auc) 

Accuracy: 0.663471778487753
Precision: 0.8651026392961877
Recall: 0.5221238938053098
AUC with score: 0.9947429906542057

# LOS LOGITS DE TEST!!
train_drug_logs = pd.DataFrame(delta_logits_matrix.loc[:,selected_drug_name]).reset_index()
train_drug_logs.columns  = ["GO_term","probability"]
train_drug_logs = train_drug_logs.merge(real_go_info_svm, on="GO_term")
train_drug_logs.sort_values(by=["probability"], ascending=False)

Final model SVM

Once the models have been cross-validated we create the final models using all samples…

GO_terms_auc_svm_final = {}
GO_terms_aupr_svm_final = {}
GO_terms_precision_svm_final = {}
models_svm = {}

# Perform logistics
for goterm in sparseGO_terms:
    #print(goterm)
    goterm_drugs = slim_matrix.loc[[goterm+"_"+str(1)]].values.flatten()
    
    if sum(goterm_drugs) <= 8:
        continue

    list_nodes = []
    for i in range(1,7):
        list_nodes.append(goterm+"_"+str(i))

    score = attribution_data_annotated.loc[list_nodes].T
    score_mod = score.divide(score.std()).fillna(0)
    
    X_train = score_mod
    X_test = score_mod
    y_train = goterm_drugs
    y_test = goterm_drugs
    
    #gamma = 1/(X_train.shape[1]*X_train.to_numpy().var())
    gamma="scale"
    C=1
    
    
        
    svm_model = svm.SVC(C=C,gamma=gamma, kernel='rbf',
                           class_weight="balanced",
                            tol=0.001,
                            probability=True,
                           random_state=1234)
    # fit the model with data
    svm_model.fit(X_train,y_train)
    y_pred=svm_model.predict(X_test)

    #auc
    y_pred_proba = svm_model.predict_proba(X_test)[::,1]  # platt values
    #y_pred_proba = svm_model.decision_function(X_test)
    
    GO_terms_auc_svm_final[goterm] = metrics.roc_auc_score(y_test, y_pred_proba)

    precision, recall, thresholds = metrics.precision_recall_curve(y_test, y_pred_proba)
    GO_terms_aupr_svm_final[goterm] = metrics.auc(recall, precision)
    GO_terms_precision_svm_final[goterm] = metrics.precision_score(y_test, y_pred)
    models_svm[goterm]=svm_model

len(models_svm)

939

Final model AUC

GO_terms_auc_svm_df_final = pd.DataFrame(list(GO_terms_auc_svm_final.items()),columns = ['goterm','auc']).set_index("goterm")
GO_terms_auc_svm_df_final = GO_terms_auc_svm_df_final.dropna()
GO_terms_auc_svm_df_final.sort_values(by=["auc"], ascending=False)

	auc
goterm
GO:0036289	1.000000
GO:0060440	0.998540
GO:0043162	0.995455
GO:0070059	0.994760
GO:0071364	0.994109
GO:1901029	0.994048
GO:0072384	0.993636
GO:0051453	0.993393
GO:0001556	0.991972
GO:0090201	0.991808
GO:0010750	0.990909
GO:0016573	0.990783
GO:1903800	0.990573
GO:1904950	0.989945
GO:1902455	0.989091
GO:0042149	0.987697
GO:0034983	0.987273
GO:1990403	0.985909
GO:0071353	0.985587
GO:0006275	0.984226
GO:0010971	0.984091
GO:0006869	0.983409
GO:0001779	0.983182
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GO:1902904	0.806512
GO:0030097	0.806125
GO:0007399	0.805949
GO:0050853	0.805230
GO:0051726	0.804914
GO:0008360	0.804780
GO:0050863	0.804772
GO:0010629	0.804702
GO:0032880	0.804305
GO:0021795	0.804198
GO:0046488	0.804184
GO:0031032	0.804004
GO:0045595	0.803077
GO:0006936	0.802344
GO:0045793	0.802149
GO:0071222	0.801980
GO:0051897	0.801416
GO:0006606	0.800953
GO:0006886	0.800872
GO:0030307	0.800490
GO:0048738	0.800331
GO:0010821	0.800220
GO:0051247	0.800154
GO:0042752	0.800120
GO:0032835	0.800025
GO:0033138	0.799982
GO:1903578	0.799701
GO:0050673	0.798946
GO:0006997	0.798672
GO:0060341	0.798662
GO:0006281	0.798556
GO:0042391	0.798475
GO:0050808	0.797394
GO:0007267	0.797360
GO:0050865	0.797107
GO:0018105	0.797070
GO:0060560	0.796569
GO:0071478	0.796131
GO:0018107	0.796045
GO:0019216	0.795977
GO:0023061	0.795969
GO:0036473	0.795897
GO:0051147	0.795455
GO:0006996	0.794900
GO:0030217	0.794761
GO:0070527	0.794579
GO:0050804	0.793936
GO:0060021	0.793808
GO:0045321	0.793792
GO:0046034	0.792891
GO:1904646	0.792812
GO:0030182	0.792624
GO:0002764	0.790893
GO:0007596	0.790844
GO:0043542	0.790474
GO:0006355	0.790400
GO:0010638	0.790227
GO:0042110	0.789916
GO:2000811	0.789519
GO:0045785	0.789271
GO:0001952	0.789204
GO:0048709	0.787833
GO:0016192	0.787802
GO:0002320	0.787705
GO:0045944	0.787650
GO:0035051	0.787216
GO:0070663	0.786907
GO:0046486	0.786765
GO:0006914	0.786701
GO:0071407	0.786480
GO:0048468	0.786471
GO:0043065	0.786229
GO:1902532	0.786009
GO:0033044	0.785934
GO:0031333	0.785379
GO:0071417	0.785307
GO:0016241	0.785238
GO:0007268	0.785105
GO:0007010	0.785047
GO:0002443	0.783904
GO:2000270	0.783308
GO:0001764	0.782709
GO:0051174	0.781935
GO:0034329	0.781439
GO:0043549	0.781269
GO:0010595	0.781136
GO:2001020	0.780899
GO:0050776	0.780250
GO:0007159	0.780220
GO:0048041	0.780105
GO:0016236	0.779569
GO:0048638	0.778556
GO:0042551	0.778521
GO:0007517	0.778474
GO:0032869	0.777921
GO:0051649	0.777222
GO:0009725	0.777056
GO:0030855	0.776398
GO:0002040	0.776347
GO:0071310	0.775759
GO:0042063	0.775499
GO:0009266	0.775262
GO:0048469	0.774721
GO:0042307	0.774054
GO:0032879	0.772742
GO:0002376	0.772696
GO:0055082	0.772549
GO:0016070	0.772150
GO:0060840	0.771853
GO:0010632	0.771656
GO:0007219	0.771429
GO:0051341	0.770833
GO:0060416	0.770267
GO:0090050	0.770256
GO:0002274	0.770035
GO:0009968	0.768538
GO:0009416	0.768293
GO:0009653	0.767978
GO:0030183	0.767941
GO:0007507	0.766819
GO:0007283	0.766625
GO:0048589	0.766590
GO:0050790	0.766284
GO:0065003	0.765562
GO:0030032	0.765559
GO:0048103	0.765258
GO:0006954	0.764565
GO:0048878	0.764329
GO:0007420	0.764092
GO:0030168	0.762921
GO:0006629	0.761422
GO:0006644	0.760398
GO:0001525	0.760172
GO:0120035	0.759979
GO:0034103	0.759958
GO:0014070	0.759563
GO:0044255	0.758471
GO:0051098	0.758377
GO:0051641	0.757853
GO:0034599	0.756607
GO:0043473	0.756079
GO:0036092	0.755500
GO:0048863	0.755435
GO:2000352	0.754950
GO:0030162	0.754327
GO:0042325	0.754119
GO:0008202	0.754059
GO:0033628	0.753988
GO:0051146	0.753713
GO:0010243	0.753077
GO:0043524	0.752485
GO:0003014	0.752381
GO:0002684	0.752271
GO:0001763	0.751863
GO:0051145	0.751530
GO:0045596	0.750733
GO:0000226	0.750411
GO:0031175	0.749603
GO:0007155	0.749178
GO:0002064	0.748667
GO:0045597	0.748593
GO:0040008	0.748313
GO:0060485	0.746706
GO:0006508	0.746456
GO:0097191	0.746084
GO:0016477	0.745481
GO:0005975	0.745307
GO:0043066	0.745136
GO:0050890	0.744664
GO:0007265	0.744598
GO:0032092	0.743751
GO:0051017	0.743352
GO:0007005	0.742652
GO:0043434	0.742583
GO:0003158	0.742221
GO:0042113	0.742005
GO:0120162	0.741508
GO:0051881	0.740169
GO:0030522	0.740000
GO:0007160	0.739980
GO:0048511	0.737766
GO:0044281	0.736585
GO:0007568	0.736500
GO:0007610	0.735604
GO:0035556	0.734890
GO:0048017	0.734281
GO:0006325	0.734050
GO:0006915	0.733434
GO:0008284	0.731624
GO:0045165	0.731183
GO:0002682	0.730932
GO:0022414	0.730731
GO:0097190	0.730349
GO:0051494	0.729426
GO:0051128	0.728822
GO:0043254	0.728497
GO:0098609	0.727096
GO:0006338	0.726229
GO:0007423	0.725167
GO:0001649	0.721908
GO:0048661	0.721041
GO:0010941	0.719507
GO:1900407	0.716777
GO:0007166	0.714859
GO:0002683	0.708778
GO:1902903	0.708773
GO:0008285	0.705969
GO:0030900	0.704312
GO:0034504	0.704188
GO:0033365	0.703782
GO:0070997	0.703482
GO:0033043	0.697664
GO:0051249	0.687925
GO:0008104	0.675076
GO:0033002	0.627125
GO:0042593	0.364293
GO:0071887	0.359703
GO:0044262	0.349744
GO:0051640	0.348052
GO:0051000	0.299934
GO:0050778	0.290903
GO:0007156	0.278928
GO:0008361	0.265902
GO:0070301	0.262988
GO:0022407	0.258553
GO:0015031	0.257560
GO:0043525	0.248848
GO:0051353	0.242570
GO:0043086	0.239824
GO:0045471	0.229314
GO:0051497	0.208992
GO:0031529	0.208648
GO:0099504	0.206822
GO:0043547	0.194217
GO:1904659	0.191457
GO:0031334	0.184335
GO:0046677	0.183479
GO:0015980	0.180476
GO:0060291	0.175234
GO:0009259	0.166290
GO:0060173	0.162212
GO:0042632	0.145299
GO:0046890	0.144186
GO:0032760	0.142722
GO:0051302	0.135000
GO:0031295	0.134696
GO:0019318	0.123006
GO:0010951	0.120040
GO:0021987	0.119137
GO:0006163	0.118024
GO:0030041	0.107955
GO:0001892	0.106324
GO:0030512	0.105991
GO:0060079	0.105991
GO:0050770	0.098547
GO:0051928	0.097553
GO:0031397	0.094042
GO:0060041	0.082956
GO:0051047	0.076258
GO:0019722	0.041730
GO:0090042	0.035699

sns.set(rc={'figure.figsize':(6,4)})
perc = str(round((100*len(GO_terms_auc_svm_df_final[GO_terms_auc_svm_df_final["auc"]>0.7])/len(GO_terms_auc_svm_df_final)),2))+"%"
N, bins, patches = plt.hist(GO_terms_auc_svm_df_final, color=CB_color_cycle[6],bins=50, linewidth=0.1)

for i in range(0,len(bins)-1):
    if bins[i]>0.7:
        patches[i].set_facecolor(CB_color_cycle[2])

plt.xlabel("AUC (logistic 1)", fontsize=16)  
plt.title(perc, fontsize=16)
# con el que mejor funciona es con la suma normal del attribution 

Text(0.5, 1.0, '94.68%')

Final model AUPR

GO_terms_aupr_svm_df_final = pd.DataFrame(list(GO_terms_aupr_svm_final.items()),columns = ['goterm','aupr']).set_index("goterm")
GO_terms_aupr_svm_df_final = GO_terms_aupr_svm_df_final.dropna()
GO_terms_aupr_svm_df_final.sort_values(by=["aupr"], ascending=False).head()

	aupr
goterm
GO:0036289	1.000000
GO:0050896	0.995438
GO:0043170	0.989680
GO:0006807	0.987396
GO:0060440	0.978213

# TENGO PROBLEMA CON EL RECALL 
sns.set(rc={'figure.figsize':(5,3)})
perc = str(round((100*len(GO_terms_aupr_svm_df_final[GO_terms_aupr_svm_df_final["aupr"]>0.7])/len(GO_terms_aupr_svm_df_final)),2))+"%"
N, bins, patches = plt.hist(GO_terms_aupr_svm_df_final, color=CB_color_cycle[6],bins=50, linewidth=0.1)
for i in range(0,len(bins)-1):
    if bins[i]>0.7:
        patches[i].set_facecolor(CB_color_cycle[3])

plt.xlabel("AUPR", fontsize=16)  
plt.title(perc, fontsize=16)

Text(0.5, 1.0, '20.02%')

Predict for a new drug

Make predictions

unknown = list(set(attribution_data_all.columns)-set(attribution_data_annotated.columns))

Get the probabilities for all unknown drugs

predictions = {}
distances = {}
probabilities_unknown = pd.DataFrame()
preds_unknown = pd.DataFrame()

for drug in unknown:
    probabilities = {}
    for goterm in models_svm.keys():

        list_nodes = list(models_svm[goterm].feature_names_in_) # Extract the feature names from the model (those are the attributions we need)

        score = attribution_data_all.loc[list_nodes][drug].to_frame().T 
        score_mod = score.divide(attribution_data_annotated.loc[list_nodes].T.std()).fillna(0) #divide by std of each neuron, only use drugs that trained the models

        predictions[goterm]=models_svm[goterm].predict(score_mod)
        probabilities[goterm] = models_svm[goterm].predict_proba(score_mod)[::,1]  # platt values
        # distances[goterm] = models_svm[goterm].decision_function(score_mod)
        
    drug_probs = pd.DataFrame.from_dict(probabilities).T
    drug_probs.columns = [drug]
    drug_preds = pd.DataFrame.from_dict(predictions).T
    drug_preds.columns = [drug]    
    probabilities_unknown = pd.concat([probabilities_unknown,drug_probs], axis=1)
    preds_unknown = pd.concat([preds_unknown,drug_preds], axis=1)
    print(drug)

Study drug with unknown MOA

Choose drug with unknown MOA…

combobox_u = interactive(f, drug=widgets.Combobox(options=unknown))

predictions_nodes = []
for goterm in list(platt_matrix.index):
    predictions_nodes.append(goterm+"_"+str(1))

# add names to go terms
real_go_info_svm= real_go_info[real_go_info.GO_term.isin(predictions_nodes)]
real_go_info_svm.GO_term = real_go_info_svm.GO_term.str.replace("_1","")

display(combobox_u)

selected_drug_u_name = combobox_u.result

predictions_df = pd.DataFrame.from_dict(preds_unknown.loc[:,selected_drug_u_name]).reset_index()
predictions_df.columns  = ["GO_term","predictions"]

probabilities_df = pd.DataFrame.from_dict(probabilities_unknown.loc[:,selected_drug_u_name]).reset_index()
probabilities_df.columns  = ["GO_term","probability"]
probabilities_df = probabilities_df.merge(real_go_info_svm, on="GO_term")
probabilities_df = probabilities_df.merge(predictions_df, on="GO_term")
probabilities_df.loc[probabilities_df["layer_number"] <=3].sort_values(by=["probability"], ascending=False).head(200)

	GO_term	probability	Name	layer_number	predictions
820	GO:0033993	0.809556	Response to lipid (1)	3.0	1.0
223	GO:0018108	0.783375	Peptidyl-tyrosine phosphorylation (1)	3.0	1.0
573	GO:0010629	0.742615	Negative regulation of gene expression (1)	3.0	1.0
106	GO:0071900	0.725062	Regulation of protein serine/threonine kinase activity (1)	2.0	1.0
624	GO:0010628	0.702924	Positive regulation of gene expression (1)	3.0	1.0
74	GO:0001817	0.687600	Regulation of cytokine production (1)	3.0	1.0
44	GO:0048812	0.672129	Neuron projection morphogenesis (1)	3.0	1.0
224	GO:0046777	0.661031	Protein autophosphorylation (1)	1.0	1.0
99	GO:0001934	0.658374	Positive regulation of protein phosphorylation (1)	3.0	1.0
570	GO:0045597	0.628072	Positive regulation of cell differentiation (1)	3.0	1.0
839	GO:0031047	0.553888	Gene silencing by rna (1)	2.0	1.0
100	GO:0033138	0.542770	Positive regulation of peptidyl-serine phosphorylation (1)	1.0	1.0
888	GO:0034976	0.540483	Response to endoplasmic reticulum stress (1)	3.0	1.0
633	GO:0051301	0.535293	Cell division (1)	2.0	1.0
821	GO:0034097	0.533600	Response to cytokine (1)	3.0	1.0
423	GO:1902533	0.530199	Positive regulation of intracellular signal transduction (1)	2.0	1.0
596	GO:0060341	0.523390	Regulation of cellular localization (1)	3.0	0.0
729	GO:0120035	0.514954	Regulation of plasma membrane bounded cell projection organization (1)	3.0	1.0
568	GO:0008284	0.513916	Positive regulation of cell population proliferation (1)	2.0	1.0
558	GO:0016032	0.500000	Viral process (1)	3.0	1.0
641	GO:0071417	0.494543	Cellular response to organonitrogen compound (1)	3.0	1.0
353	GO:0006954	0.482633	Inflammatory response (1)	3.0	1.0
9	GO:0043408	0.476159	Regulation of mapk cascade (1)	2.0	1.0
808	GO:1902532	0.449154	Negative regulation of intracellular signal transduction (1)	3.0	1.0
11	GO:0043406	0.445845	Positive regulation of map kinase activity (1)	1.0	1.0
8	GO:0000165	0.443783	Mapk cascade (1)	3.0	0.0
358	GO:0007005	0.427023	Mitochondrion organization (1)	3.0	1.0
134	GO:0002366	0.423235	Leukocyte activation involved in immune response (1)	3.0	1.0
221	GO:0018105	0.422261	Peptidyl-serine phosphorylation (1)	2.0	0.0
285	GO:0051051	0.421500	Negative regulation of transport (1)	3.0	1.0
191	GO:0045944	0.420524	Positive regulation of transcription by rna polymerase ii (1)	2.0	1.0
654	GO:0090398	0.416328	Cellular senescence (1)	1.0	1.0
847	GO:0045055	0.414665	Regulated exocytosis (1)	2.0	1.0
342	GO:2001243	0.411574	Negative regulation of intrinsic apoptotic signaling pathway (1)	2.0	1.0
510	GO:0042063	0.408279	Gliogenesis (1)	3.0	1.0
788	GO:0009410	0.407937	Response to xenobiotic stimulus (1)	2.0	1.0
824	GO:0071363	0.407544	Cellular response to growth factor stimulus (1)	3.0	1.0
496	GO:0048608	0.406699	Reproductive structure development (1)	2.0	1.0
528	GO:0007565	0.406022	Female pregnancy (1)	2.0	1.0
896	GO:0097193	0.400210	Intrinsic apoptotic signaling pathway (1)	3.0	1.0
76	GO:0001819	0.388417	Positive regulation of cytokine production (1)	2.0	1.0
48	GO:0001525	0.386728	Angiogenesis (1)	2.0	1.0
300	GO:0032386	0.384834	Regulation of intracellular transport (1)	2.0	1.0
906	GO:0043549	0.378711	Regulation of kinase activity (1)	3.0	0.0
662	GO:0031648	0.377411	Protein destabilization (1)	0.0	1.0
516	GO:0007423	0.372363	Sensory organ development (1)	3.0	1.0
461	GO:0050804	0.367884	Modulation of chemical synaptic transmission (1)	3.0	1.0
104	GO:0006469	0.364467	Negative regulation of protein kinase activity (1)	2.0	1.0
620	GO:0051098	0.363823	Regulation of binding (1)	3.0	1.0
86	GO:0072006	0.358531	Nephron development (1)	2.0	1.0
473	GO:0008584	0.350294	Male gonad development (1)	1.0	1.0
536	GO:0007610	0.346612	Behavior (1)	3.0	0.0
693	GO:1904646	0.344997	Cellular response to amyloid-beta (1)	0.0	1.0
376	GO:0007015	0.344878	Actin filament organization (1)	3.0	1.0
552	GO:0033365	0.343027	Protein localization to organelle (1)	3.0	1.0
774	GO:0030216	0.342203	Keratinocyte differentiation (1)	2.0	1.0
639	GO:0060326	0.341919	Cell chemotaxis (1)	2.0	1.0
154	GO:0050778	0.335649	Positive regulation of immune response (1)	3.0	0.0
480	GO:0048565	0.333289	Digestive tract development (1)	1.0	1.0
794	GO:0043434	0.332497	Response to peptide hormone (1)	3.0	1.0
651	GO:0050808	0.322913	Synapse organization (1)	3.0	1.0
772	GO:0060485	0.320964	Mesenchyme development (1)	3.0	1.0
406	GO:0048041	0.319689	Focal adhesion assembly (1)	1.0	1.0
698	GO:0070663	0.318299	Regulation of leukocyte proliferation (1)	2.0	1.0
804	GO:0030855	0.317904	Epithelial cell differentiation (1)	3.0	0.0
538	GO:0048266	0.308838	Behavioral response to pain (1)	0.0	1.0
225	GO:0006470	0.305848	Protein dephosphorylation (1)	3.0	1.0
719	GO:0043244	0.305739	Regulation of protein-containing complex disassembly (1)	2.0	1.0
725	GO:0070997	0.305130	Neuron death (1)	2.0	0.0
532	GO:0007596	0.304871	Blood coagulation (1)	3.0	1.0
806	GO:0051056	0.302828	Regulation of small gtpase mediated signal transduction (1)	3.0	1.0
433	GO:0097191	0.302625	Extrinsic apoptotic signaling pathway (1)	3.0	1.0
574	GO:0008285	0.300631	Negative regulation of cell population proliferation (1)	3.0	0.0
49	GO:0001569	0.300115	Branching involved in blood vessel morphogenesis (1)	0.0	1.0
926	GO:0060020	0.297104	Bergmann glial cell differentiation (1)	0.0	1.0
789	GO:0009416	0.295539	Response to light stimulus (1)	2.0	0.0
887	GO:0034504	0.294393	Protein localization to nucleus (1)	2.0	1.0
169	GO:0002764	0.292841	Immune response-regulating signaling pathway (1)	3.0	0.0
890	GO:0071353	0.290912	Cellular response to interleukin-4 (1)	1.0	1.0
505	GO:0007283	0.290337	Spermatogenesis (1)	2.0	1.0
513	GO:0030900	0.289030	Forebrain development (1)	3.0	1.0
241	GO:0006612	0.288796	Protein targeting to membrane (1)	1.0	1.0
644	GO:0071230	0.284292	Cellular response to amino acid stimulus (1)	1.0	1.0
712	GO:0033002	0.280632	Muscle cell proliferation (1)	2.0	1.0
317	GO:0006897	0.277768	Endocytosis (1)	3.0	1.0
230	GO:0030162	0.277751	Regulation of proteolysis (1)	3.0	0.0
607	GO:0042391	0.276380	Regulation of membrane potential (1)	3.0	0.0
689	GO:1905897	0.275961	Regulation of response to endoplasmic reticulum stress (1)	2.0	1.0
105	GO:0045860	0.275729	Positive regulation of protein kinase activity (1)	2.0	0.0
198	GO:0006260	0.275261	Dna replication (1)	3.0	1.0
548	GO:1903829	0.274893	Positive regulation of protein localization (1)	3.0	0.0
934	GO:0051258	0.274092	Protein polymerization (1)	3.0	1.0
378	GO:0031532	0.271109	Actin cytoskeleton reorganization (1)	1.0	1.0
216	GO:0045727	0.270882	Positive regulation of translation (1)	1.0	1.0
642	GO:0034599	0.270088	Cellular response to oxidative stress (1)	3.0	0.0
767	GO:0051146	0.266722	Striated muscle cell differentiation (1)	2.0	0.0
77	GO:0002718	0.262195	Regulation of cytokine production involved in immune response (1)	2.0	1.0
19	GO:0031109	0.261030	Microtubule polymerization or depolymerization (1)	2.0	1.0
584	GO:0040008	0.259597	Regulation of growth (1)	3.0	0.0
937	GO:0051640	0.257672	Organelle localization (1)	3.0	0.0
377	GO:0031032	0.257340	Actomyosin structure organization (1)	2.0	1.0
904	GO:0042113	0.254618	B cell activation (1)	3.0	0.0
133	GO:0043303	0.253768	Mast cell degranulation (1)	1.0	1.0
561	GO:0048511	0.251652	Rhythmic process (1)	3.0	1.0
243	GO:0006606	0.250144	Protein import into nucleus (1)	1.0	1.0
836	GO:1901987	0.249107	Regulation of cell cycle phase transition (1)	3.0	0.0
758	GO:0031099	0.247922	Regeneration (1)	2.0	1.0
739	GO:1902903	0.245529	Regulation of supramolecular fiber organization (1)	3.0	1.0
323	GO:0016236	0.245469	Macroautophagy (1)	3.0	1.0
478	GO:0048568	0.244894	Embryonic organ development (1)	3.0	0.0
103	GO:0042531	0.243583	Positive regulation of tyrosine phosphorylation of stat protein (1)	0.0	1.0
598	GO:0043254	0.240098	Regulation of protein-containing complex assembly (1)	3.0	1.0
864	GO:0030183	0.236405	B cell differentiation (1)	1.0	0.0
301	GO:0032388	0.235842	Positive regulation of intracellular transport (1)	1.0	0.0
692	GO:0010595	0.235403	Positive regulation of endothelial cell migration (1)	2.0	1.0
865	GO:0030217	0.234830	T cell differentiation (1)	3.0	0.0
20	GO:0070507	0.234661	Regulation of microtubule cytoskeleton organization (1)	2.0	1.0
111	GO:0031069	0.234249	Hair follicle morphogenesis (1)	0.0	1.0
363	GO:0051494	0.232679	Negative regulation of cytoskeleton organization (1)	2.0	1.0
226	GO:0035304	0.232643	Regulation of protein dephosphorylation (1)	2.0	1.0
913	GO:0090630	0.231968	Activation of gtpase activity (1)	0.0	1.0
733	GO:0030335	0.231192	Positive regulation of cell migration (1)	3.0	0.0
196	GO:0006357	0.230696	Regulation of transcription by rna polymerase ii (1)	3.0	1.0
524	GO:0007519	0.230417	Skeletal muscle tissue development (1)	2.0	1.0
53	GO:0001570	0.230416	Vasculogenesis (1)	1.0	1.0
26	GO:1901990	0.229708	Regulation of mitotic cell cycle phase transition (1)	2.0	0.0
33	GO:0000423	0.229518	Mitophagy (1)	1.0	1.0
615	GO:0035265	0.227576	Organ growth (1)	2.0	0.0
84	GO:0001822	0.227091	Kidney development (1)	3.0	0.0
151	GO:0006959	0.225795	Humoral immune response (1)	2.0	1.0
244	GO:0042307	0.225164	Positive regulation of protein import into nucleus (1)	0.0	1.0
24	GO:0007346	0.224934	Regulation of mitotic cell cycle (1)	3.0	0.0
162	GO:0060374	0.223666	Mast cell differentiation (1)	0.0	1.0
533	GO:0030168	0.219923	Platelet activation (1)	2.0	1.0
152	GO:0045087	0.218376	Innate immune response (1)	3.0	0.0
523	GO:0007517	0.217930	Muscle organ development (1)	3.0	0.0
138	GO:0002683	0.217495	Negative regulation of immune system process (1)	3.0	0.0
319	GO:0006909	0.217477	Phagocytosis (1)	2.0	1.0
18	GO:0000226	0.216530	Microtubule cytoskeleton organization (1)	3.0	0.0
622	GO:0043086	0.212387	Negative regulation of catalytic activity (1)	3.0	1.0
898	GO:0035924	0.211788	Cellular response to vascular endothelial growth factor stimulus (1)	2.0	1.0
187	GO:0071897	0.211315	Dna biosynthetic process (1)	2.0	0.0
517	GO:0043586	0.209986	Tongue development (1)	1.0	1.0
606	GO:0048638	0.209981	Regulation of developmental growth (1)	2.0	0.0
736	GO:0009617	0.209127	Response to bacterium (1)	3.0	0.0
691	GO:0043542	0.207450	Endothelial cell migration (1)	3.0	0.0
321	GO:0010507	0.205494	Negative regulation of autophagy (1)	1.0	1.0
149	GO:0050853	0.205417	B cell receptor signaling pathway (1)	1.0	0.0
885	GO:1900180	0.205300	Regulation of protein localization to nucleus (1)	1.0	1.0
907	GO:0051881	0.202707	Regulation of mitochondrial membrane potential (1)	1.0	0.0
171	GO:0003014	0.201637	Renal system process (1)	2.0	1.0
211	GO:0031507	0.200984	Heterochromatin assembly (1)	1.0	1.0
64	GO:0071456	0.200564	Cellular response to hypoxia (1)	1.0	0.0
694	GO:0032869	0.197755	Cellular response to insulin stimulus (1)	2.0	0.0
779	GO:0008544	0.196206	Epidermis development (1)	3.0	0.0
634	GO:0061024	0.193065	Membrane organization (1)	2.0	0.0
50	GO:0002040	0.192737	Sprouting angiogenesis (1)	1.0	0.0
610	GO:0031333	0.191284	Negative regulation of protein-containing complex assembly (1)	2.0	1.0
657	GO:0045165	0.191259	Cell fate commitment (1)	3.0	0.0
435	GO:0016055	0.190638	Wnt signaling pathway (1)	2.0	0.0
569	GO:0030307	0.190101	Positive regulation of cell growth (1)	2.0	1.0
200	GO:0006281	0.189187	Dna repair (1)	2.0	0.0
63	GO:0001666	0.188575	Response to hypoxia (1)	2.0	1.0
560	GO:0043473	0.188137	Pigmentation (1)	2.0	1.0
521	GO:0035051	0.187551	Cardiocyte differentiation (1)	2.0	1.0
690	GO:2001020	0.187450	Regulation of response to dna damage stimulus (1)	2.0	0.0
346	GO:0006936	0.187431	Muscle contraction (1)	3.0	1.0
328	GO:0043065	0.184805	Positive regulation of apoptotic process (1)	2.0	0.0
650	GO:0034329	0.183554	Cell junction assembly (1)	2.0	0.0
262	GO:0045429	0.183059	Positive regulation of nitric oxide biosynthetic process (1)	0.0	1.0
117	GO:0060562	0.183024	Epithelial tube morphogenesis (1)	2.0	0.0
583	GO:0032967	0.182852	Positive regulation of collagen biosynthetic process (1)	0.0	1.0
208	GO:0006325	0.182003	Chromatin organization (1)	3.0	0.0
209	GO:0006338	0.181855	Chromatin remodeling (1)	2.0	1.0
882	GO:0032147	0.181843	Activation of protein kinase activity (1)	1.0	0.0
7	GO:0000122	0.181449	Negative regulation of transcription by rna polymerase ii (1)	1.0	0.0
911	GO:0060416	0.180831	Response to growth hormone (1)	1.0	1.0
121	GO:0090050	0.180206	Positive regulation of cell migration involved in sprouting angiogenesis (1)	0.0	1.0
467	GO:0009791	0.179613	Post-embryonic development (1)	1.0	0.0
870	GO:0070527	0.179383	Platelet aggregation (1)	1.0	1.0
781	GO:0008625	0.179216	Extrinsic apoptotic signaling pathway via death domain receptors (1)	1.0	1.0
702	GO:0048146	0.179210	Positive regulation of fibroblast proliferation (1)	0.0	1.0
785	GO:0009266	0.178944	Response to temperature stimulus (1)	2.0	1.0
316	GO:0033157	0.178583	Regulation of intracellular protein transport (1)	1.0	0.0
472	GO:0001553	0.178541	Luteinization (1)	0.0	1.0
174	GO:0010613	0.176998	Positive regulation of cardiac muscle hypertrophy (1)	1.0	1.0
842	GO:0071407	0.176595	Cellular response to organic cyclic compound (1)	3.0	0.0
834	GO:0035195	0.176389	Gene silencing by mirna (1)	1.0	0.0
504	GO:0048709	0.176248	Oligodendrocyte differentiation (1)	2.0	1.0
54	GO:2001214	0.175971	Positive regulation of vasculogenesis (1)	0.0	1.0
600	GO:0010632	0.174413	Regulation of epithelial cell migration (1)	3.0	0.0
682	GO:0007026	0.174040	Negative regulation of microtubule depolymerization (1)	0.0	1.0
276	GO:0016567	0.173194	Protein ubiquitination (1)	3.0	0.0
881	GO:0031929	0.171840	Tor signaling (1)	2.0	1.0
52	GO:0001541	0.171713	Ovarian follicle development (1)	1.0	0.0
310	GO:0051924	0.171261	Regulation of calcium ion transport (1)	3.0	0.0
899	GO:0035994	0.170901	Response to muscle stretch (1)	1.0	1.0
32	GO:0000422	0.170612	Autophagy of mitochondrion (1)	2.0	1.0
703	GO:0048661	0.169791	Positive regulation of smooth muscle cell proliferation (1)	1.0	0.0
447	GO:0007173	0.169218	Epidermal growth factor receptor signaling pathway (1)	2.0	0.0

sum(probabilities_df["predictions"] ==1)

288

sum(probabilities_df["predictions"] ==0)

651

Probability < 0.5 doesn’t mean it does not belong to the class, a probability of for example 0.2 can represent a 1 (annotated to MoA)

Modify probabilities

Take into account the annotations each GO term has (general GO terms are easier to predict as they have more annotations)

For drug with unknown MOA…

sum_annotations = slim_matrix_single_neuron.T.sum()/slim_matrix_single_neuron.shape[1]
logits_apriori = np.log(sum_annotations/(1-sum_annotations))

logits_apost= np.log(probabilities_df["probability"]/(1-probabilities_df["probability"]))
delta_logits =logits_apost.to_numpy()- logits_apriori.to_numpy()
delta_logits_df = pd.DataFrame(delta_logits)
delta_logits_df.columns = ["delta_logits"]
probabilities_mod = probabilities_df.merge(delta_logits_df, left_index=True,right_index=True)

probabilities_mod.loc[probabilities_mod["predictions"] ==1].loc[probabilities_mod["layer_number"] <= 7].sort_values(by=["delta_logits"], ascending=False)

	GO_term	probability	Name	layer_number	predictions	delta_logits
839	GO:0031047	0.553888	Gene silencing by rna (1)	2.0	1.0	2.192458
662	GO:0031648	0.377411	Protein destabilization (1)	0.0	1.0	2.092834
106	GO:0071900	0.725062	Regulation of protein serine/threonine kinase activity (1)	2.0	1.0	1.944569
33	GO:0000423	0.229518	Mitophagy (1)	1.0	1.0	1.880007
538	GO:0048266	0.308838	Behavioral response to pain (1)	0.0	1.0	1.857031
913	GO:0090630	0.231968	Activation of gtpase activity (1)	0.0	1.0	1.793943
216	GO:0045727	0.270882	Positive regulation of translation (1)	1.0	1.0	1.746070
223	GO:0018108	0.783375	Peptidyl-tyrosine phosphorylation (1)	3.0	1.0	1.672826
719	GO:0043244	0.305739	Regulation of protein-containing complex disassembly (1)	2.0	1.0	1.646098
241	GO:0006612	0.288796	Protein targeting to membrane (1)	1.0	1.0	1.626841
104	GO:0006469	0.364467	Negative regulation of protein kinase activity (1)	2.0	1.0	1.593794
888	GO:0034976	0.540483	Response to endoplasmic reticulum stress (1)	3.0	1.0	1.575980
890	GO:0071353	0.290912	Cellular response to interleukin-4 (1)	1.0	1.0	1.575256
638	GO:0033554	0.884331	Cellular response to stress (1)	4.0	1.0	1.573975
820	GO:0033993	0.809556	Response to lipid (1)	3.0	1.0	1.551573
74	GO:0001817	0.687600	Regulation of cytokine production (1)	3.0	1.0	1.534715
682	GO:0007026	0.174040	Negative regulation of microtubule depolymerization (1)	0.0	1.0	1.533781
528	GO:0007565	0.406022	Female pregnancy (1)	2.0	1.0	1.478881
224	GO:0046777	0.661031	Protein autophosphorylation (1)	1.0	1.0	1.453821
544	GO:0060179	0.161090	Male mating behavior (1)	0.0	1.0	1.440901
32	GO:0000422	0.170612	Autophagy of mitochondrion (1)	2.0	1.0	1.409881
774	GO:0030216	0.342203	Keratinocyte differentiation (1)	2.0	1.0	1.406530
44	GO:0048812	0.672129	Neuron projection morphogenesis (1)	3.0	1.0	1.385004
472	GO:0001553	0.178541	Luteinization (1)	0.0	1.0	1.373326
77	GO:0002718	0.262195	Regulation of cytokine production involved in immune response (1)	2.0	1.0	1.372827
900	GO:0042060	0.612497	Wound healing (1)	4.0	1.0	1.325911
385	GO:0060632	0.143694	Regulation of microtubule-based movement (1)	1.0	1.0	1.306104
726	GO:0065003	0.654123	Protein-containing complex assembly (1)	4.0	1.0	1.304384
926	GO:0060020	0.297104	Bergmann glial cell differentiation (1)	0.0	1.0	1.288696
582	GO:1902459	0.140358	Positive regulation of stem cell population maintenance (1)	0.0	1.0	1.278722
63	GO:0001666	0.188575	Response to hypoxia (1)	2.0	1.0	1.276928
342	GO:2001243	0.411574	Negative regulation of intrinsic apoptotic signaling pathway (1)	2.0	1.0	1.262448
573	GO:0010629	0.742615	Negative regulation of gene expression (1)	3.0	1.0	1.216446
480	GO:0048565	0.333289	Digestive tract development (1)	1.0	1.0	1.203775
174	GO:0010613	0.176998	Positive regulation of cardiac muscle hypertrophy (1)	1.0	1.0	1.199403
676	GO:0030282	0.116755	Bone mineralization (1)	1.0	1.0	1.177409
49	GO:0001569	0.300115	Branching involved in blood vessel morphogenesis (1)	0.0	1.0	1.170620
899	GO:0035994	0.170901	Response to muscle stretch (1)	1.0	1.0	1.156962
443	GO:0035860	0.114067	Glial cell-derived neurotrophic factor receptor signaling pathway (1)	0.0	1.0	1.151083
847	GO:0045055	0.414665	Regulated exocytosis (1)	2.0	1.0	1.096844
134	GO:0002366	0.423235	Leukocyte activation involved in immune response (1)	3.0	1.0	1.076788
181	GO:0006139	0.875555	Nucleobase-containing compound metabolic process (1)	6.0	1.0	1.061941
689	GO:1905897	0.275961	Regulation of response to endoplasmic reticulum stress (1)	2.0	1.0	1.052784
38	GO:0000902	0.813929	Cell morphogenesis (1)	4.0	1.0	1.052132
654	GO:0090398	0.416328	Cellular senescence (1)	1.0	1.0	1.048427
693	GO:1904646	0.344997	Cellular response to amyloid-beta (1)	0.0	1.0	1.043235
100	GO:0033138	0.542770	Positive regulation of peptidyl-serine phosphorylation (1)	1.0	1.0	1.018799
745	GO:0009653	0.877370	Anatomical structure morphogenesis (1)	5.0	1.0	1.014649
198	GO:0006260	0.275261	Dna replication (1)	3.0	1.0	1.007970
133	GO:0043303	0.253768	Mast cell degranulation (1)	1.0	1.0	0.981406
368	GO:0060271	0.126831	Cilium assembly (1)	3.0	1.0	0.970313
285	GO:0051051	0.421500	Negative regulation of transport (1)	3.0	1.0	0.964316
626	GO:0051649	0.802013	Establishment of localization in cell (1)	4.0	1.0	0.957089
378	GO:0031532	0.271109	Actin cytoskeleton reorganization (1)	1.0	1.0	0.947004
651	GO:0050808	0.322913	Synapse organization (1)	3.0	1.0	0.943923
11	GO:0043406	0.445845	Positive regulation of map kinase activity (1)	1.0	1.0	0.939978
34	GO:1903146	0.103490	Regulation of autophagy of mitochondrion (1)	1.0	1.0	0.932003
262	GO:0045429	0.183059	Positive regulation of nitric oxide biosynthetic process (1)	0.0	1.0	0.911660
661	GO:0046326	0.165116	Positive regulation of glucose import (1)	0.0	1.0	0.907432
911	GO:0060416	0.180831	Response to growth hormone (1)	1.0	1.0	0.896690
571	GO:2000010	0.127151	Positive regulation of protein localization to cell surface (1)	0.0	1.0	0.888565
633	GO:0051301	0.535293	Cell division (1)	2.0	1.0	0.887200
483	GO:0035909	0.162013	Aorta morphogenesis (1)	1.0	1.0	0.884752
54	GO:2001214	0.175971	Positive regulation of vasculogenesis (1)	0.0	1.0	0.863534
416	GO:0035556	0.887824	Intracellular signal transduction (1)	4.0	1.0	0.862843
470	GO:0042733	0.097137	Embryonic digit morphogenesis (1)	0.0	1.0	0.861598
886	GO:0034502	0.132453	Protein localization to chromosome (1)	2.0	1.0	0.856780
111	GO:0031069	0.234249	Hair follicle morphogenesis (1)	0.0	1.0	0.832897
880	GO:0031667	0.374380	Response to nutrient levels (1)	4.0	1.0	0.819334
833	GO:0010467	0.880677	Gene expression (1)	5.0	1.0	0.817357
184	GO:0006275	0.102018	Regulation of dna replication (1)	2.0	1.0	0.816174
162	GO:0060374	0.223666	Mast cell differentiation (1)	0.0	1.0	0.815593
265	GO:0051247	0.818839	Positive regulation of protein metabolic process (1)	4.0	1.0	0.802282
821	GO:0034097	0.533600	Response to cytokine (1)	3.0	1.0	0.801775
408	GO:0007165	0.930074	Signal transduction (1)	6.0	1.0	0.801131
849	GO:0043966	0.142338	Histone h3 acetylation (1)	2.0	1.0	0.797379
112	GO:0060789	0.091132	Hair follicle placode formation (1)	0.0	1.0	0.791156
558	GO:0016032	0.500000	Viral process (1)	3.0	1.0	0.785929
151	GO:0006959	0.225795	Humoral immune response (1)	2.0	1.0	0.785159
86	GO:0072006	0.358531	Nephron development (1)	2.0	1.0	0.777591
83	GO:0002720	0.163684	Positive regulation of cytokine production involved in immune response (1)	1.0	1.0	0.776348
53	GO:0001570	0.230416	Vasculogenesis (1)	1.0	1.0	0.770097
532	GO:0007596	0.304871	Blood coagulation (1)	3.0	1.0	0.764501
567	GO:0051641	0.841801	Cellular localization (1)	5.0	1.0	0.761481
639	GO:0060326	0.341919	Cell chemotaxis (1)	2.0	1.0	0.758938
19	GO:0031109	0.261030	Microtubule polymerization or depolymerization (1)	2.0	1.0	0.746075
800	GO:0030521	0.102431	Androgen receptor signaling pathway (1)	1.0	1.0	0.729092
511	GO:0030182	0.721830	Neuron differentiation (1)	5.0	1.0	0.726501
806	GO:0051056	0.302828	Regulation of small gtpase mediated signal transduction (1)	3.0	1.0	0.724275
527	GO:0007528	0.109807	Neuromuscular junction development (1)	1.0	1.0	0.722229
794	GO:0043434	0.332497	Response to peptide hormone (1)	3.0	1.0	0.716778
99	GO:0001934	0.658374	Positive regulation of protein phosphorylation (1)	3.0	1.0	0.708241
896	GO:0097193	0.400210	Intrinsic apoptotic signaling pathway (1)	3.0	1.0	0.705651
624	GO:0010628	0.702924	Positive regulation of gene expression (1)	3.0	1.0	0.704419
722	GO:0042325	0.780828	Regulation of phosphorylation (1)	5.0	1.0	0.698909
752	GO:0043170	0.942545	Macromolecule metabolic process (1)	7.0	1.0	0.693446
517	GO:0043586	0.209986	Tongue development (1)	1.0	1.0	0.692360
182	GO:0016070	0.778891	Rna metabolic process (1)	5.0	1.0	0.687621
281	GO:0006811	0.500000	Ion transport (1)	6.0	1.0	0.686632
586	GO:2000773	0.121640	Negative regulation of cellular senescence (1)	0.0	1.0	0.685597
808	GO:1902532	0.449154	Negative regulation of intracellular signal transduction (1)	3.0	1.0	0.684968
570	GO:0045597	0.628072	Positive regulation of cell differentiation (1)	3.0	1.0	0.680798
76	GO:0001819	0.388417	Positive regulation of cytokine production (1)	2.0	1.0	0.679733
934	GO:0051258	0.274092	Protein polymerization (1)	3.0	1.0	0.677814
713	GO:0035726	0.096936	Common myeloid progenitor cell proliferation (1)	0.0	1.0	0.667842
386	GO:0007049	0.727375	Cell cycle (1)	6.0	1.0	0.665704
473	GO:0008584	0.350294	Male gonad development (1)	1.0	1.0	0.663186
813	GO:0051898	0.103239	Negative regulation of protein kinase b signaling (1)	0.0	1.0	0.653209
698	GO:0070663	0.318299	Regulation of leukocyte proliferation (1)	2.0	1.0	0.652091
729	GO:0120035	0.514954	Regulation of plasma membrane bounded cell projection organization (1)	3.0	1.0	0.631426
881	GO:0031929	0.171840	Tor signaling (1)	2.0	1.0	0.624585
496	GO:0048608	0.406699	Reproductive structure development (1)	2.0	1.0	0.619202
559	GO:0022414	0.641754	Reproductive process (1)	4.0	1.0	0.617772
562	GO:0050896	0.963328	Response to stimulus (1)	7.0	1.0	0.605804
381	GO:0008064	0.113103	Regulation of actin polymerization or depolymerization (1)	2.0	1.0	0.603158
855	GO:1903578	0.141298	Regulation of atp metabolic process (1)	1.0	1.0	0.602865
619	GO:0050790	0.825081	Regulation of catalytic activity (1)	4.0	1.0	0.598054
476	GO:0048714	0.112165	Positive regulation of oligodendrocyte differentiation (1)	0.0	1.0	0.593772
363	GO:0051494	0.232679	Negative regulation of cytoskeleton organization (1)	2.0	1.0	0.593448
652	GO:0042180	0.090402	Cellular ketone metabolic process (1)	3.0	1.0	0.590857
423	GO:1902533	0.530199	Positive regulation of intracellular signal transduction (1)	2.0	1.0	0.581066
319	GO:0006909	0.217477	Phagocytosis (1)	2.0	1.0	0.578885
353	GO:0006954	0.482633	Inflammatory response (1)	3.0	1.0	0.578336
406	GO:0048041	0.319689	Focal adhesion assembly (1)	1.0	1.0	0.577606
585	GO:0048589	0.599054	Developmental growth (1)	4.0	1.0	0.575879
461	GO:0050804	0.367884	Modulation of chemical synaptic transmission (1)	3.0	1.0	0.568933
711	GO:0019752	0.316625	Carboxylic acid metabolic process (1)	4.0	1.0	0.563481
629	GO:0051174	0.783163	Regulation of phosphorus metabolic process (1)	6.0	1.0	0.558257
82	GO:0032743	0.079738	Positive regulation of interleukin-2 production (1)	0.0	1.0	0.545266
510	GO:0042063	0.408279	Gliogenesis (1)	3.0	1.0	0.539129
730	GO:0031175	0.631148	Neuron projection development (1)	4.0	1.0	0.537147
741	GO:0016477	0.682158	Cell migration (1)	4.0	1.0	0.536651
663	GO:0050821	0.165950	Protein stabilization (1)	0.0	1.0	0.535214
343	GO:1902166	0.145254	Negative regulation of intrinsic apoptotic signaling pathway in response to dna damage by p53 class mediator (1)	0.0	1.0	0.525494
213	GO:0006396	0.268052	Rna processing (1)	4.0	1.0	0.523646
563	GO:1900272	0.063979	Negative regulation of long-term synaptic potentiation (1)	0.0	1.0	0.517858
36	GO:0000723	0.137533	Telomere maintenance (1)	1.0	1.0	0.515446
524	GO:0007519	0.230417	Skeletal muscle tissue development (1)	2.0	1.0	0.511692
93	GO:0001843	0.082584	Neural tube closure (1)	1.0	1.0	0.491845
226	GO:0035304	0.232643	Regulation of protein dephosphorylation (1)	2.0	1.0	0.490892
684	GO:0051770	0.158964	Positive regulation of nitric-oxide synthase biosynthetic process (1)	0.0	1.0	0.483868
583	GO:0032967	0.182852	Positive regulation of collagen biosynthetic process (1)	0.0	1.0	0.478922
641	GO:0071417	0.494543	Cellular response to organonitrogen compound (1)	3.0	1.0	0.475108
788	GO:0009410	0.407937	Response to xenobiotic stimulus (1)	2.0	1.0	0.474799
412	GO:0009966	0.816584	Regulation of signal transduction (1)	5.0	1.0	0.474348
735	GO:0046718	0.151222	Viral entry into host cell (1)	1.0	1.0	0.472175
266	GO:0030163	0.372430	Protein catabolic process (1)	4.0	1.0	0.453053
196	GO:0006357	0.230696	Regulation of transcription by rna polymerase ii (1)	3.0	1.0	0.447389
840	GO:0043154	0.084113	Negative regulation of cysteine-type endopeptidase activity involved in apoptotic process (1)	1.0	1.0	0.427212
299	GO:0030705	0.133167	Cytoskeleton-dependent intracellular transport (1)	3.0	1.0	0.424567
433	GO:0097191	0.302625	Extrinsic apoptotic signaling pathway (1)	3.0	1.0	0.420731
610	GO:0031333	0.191284	Negative regulation of protein-containing complex assembly (1)	2.0	1.0	0.417629
501	GO:0007507	0.480118	Heart development (1)	4.0	1.0	0.417365
901	GO:0042110	0.432222	T cell activation (1)	4.0	1.0	0.413843
260	GO:0006807	0.931522	Nitrogen compound metabolic process (1)	7.0	1.0	0.413085
785	GO:0009266	0.178944	Response to temperature stimulus (1)	2.0	1.0	0.412491
358	GO:0007005	0.427023	Mitochondrion organization (1)	3.0	1.0	0.412213
377	GO:0031032	0.257340	Actomyosin structure organization (1)	2.0	1.0	0.410068
484	GO:0007399	0.731066	Nervous system development (1)	6.0	1.0	0.409544
136	GO:0002376	0.760395	Immune system process (1)	6.0	1.0	0.409057
211	GO:0031507	0.200984	Heterochromatin assembly (1)	1.0	1.0	0.406540
20	GO:0070507	0.234661	Regulation of microtubule cytoskeleton organization (1)	2.0	1.0	0.406533
356	GO:0006996	0.787638	Organelle organization (1)	5.0	1.0	0.400534
376	GO:0007015	0.344878	Actin filament organization (1)	3.0	1.0	0.399824
173	GO:0003300	0.147997	Cardiac muscle hypertrophy (1)	2.0	1.0	0.399424
620	GO:0051098	0.363823	Regulation of binding (1)	3.0	1.0	0.394294
922	GO:0036324	0.117627	Vascular endothelial growth factor receptor-2 signaling pathway (1)	0.0	1.0	0.392320
487	GO:0030325	0.087339	Adrenal gland development (1)	0.0	1.0	0.389651
781	GO:0008625	0.179216	Extrinsic apoptotic signaling pathway via death domain receptors (1)	1.0	1.0	0.375455
608	GO:0043114	0.067682	Regulation of vascular permeability (1)	1.0	1.0	0.368329
103	GO:0042531	0.243583	Positive regulation of tyrosine phosphorylation of stat protein (1)	0.0	1.0	0.365635
102	GO:0050731	0.126350	Positive regulation of peptidyl-tyrosine phosphorylation (1)	2.0	1.0	0.364186
504	GO:0048709	0.176248	Oligodendrocyte differentiation (1)	2.0	1.0	0.355145
324	GO:0016241	0.118988	Regulation of macroautophagy (1)	2.0	1.0	0.349328
218	GO:0006468	0.771378	Protein phosphorylation (1)	5.0	1.0	0.348022
171	GO:0003014	0.201637	Renal system process (1)	2.0	1.0	0.341557
644	GO:0071230	0.284292	Cellular response to amino acid stimulus (1)	1.0	1.0	0.332289
321	GO:0010507	0.205494	Negative regulation of autophagy (1)	1.0	1.0	0.332037
533	GO:0030168	0.219923	Platelet activation (1)	2.0	1.0	0.322598
546	GO:0008104	0.599923	Protein localization (1)	5.0	1.0	0.318133
362	GO:0033043	0.644550	Regulation of organelle organization (1)	4.0	1.0	0.315090
851	GO:0070933	0.058559	Histone h4 deacetylation (1)	0.0	1.0	0.313671
460	GO:0023061	0.159325	Signal release (1)	4.0	1.0	0.312808
438	GO:0007179	0.169016	Transforming growth factor beta receptor signaling pathway (1)	1.0	1.0	0.304505
300	GO:0032386	0.384834	Regulation of intracellular transport (1)	2.0	1.0	0.296704
920	GO:0036092	0.063107	Phosphatidylinositol-3-phosphate biosynthetic process (1)	0.0	1.0	0.293434
505	GO:0007283	0.290337	Spermatogenesis (1)	2.0	1.0	0.287750
357	GO:0006997	0.062583	Nucleus organization (1)	2.0	1.0	0.284535
748	GO:0009056	0.627534	Catabolic process (1)	5.0	1.0	0.276960
48	GO:0001525	0.386728	Angiogenesis (1)	2.0	1.0	0.264850
541	GO:0008542	0.131766	Visual learning (1)	0.0	1.0	0.264386
98	GO:0001932	0.669289	Regulation of protein phosphorylation (1)	4.0	1.0	0.263137
734	GO:0051702	0.152779	Biological process involved in interaction with symbiont (1)	2.0	1.0	0.263093
243	GO:0006606	0.250144	Protein import into nucleus (1)	1.0	1.0	0.261493
513	GO:0030900	0.289030	Forebrain development (1)	3.0	1.0	0.257354
673	GO:0043392	0.109472	Negative regulation of dna binding (1)	1.0	1.0	0.255228
346	GO:0006936	0.187431	Muscle contraction (1)	3.0	1.0	0.250863
552	GO:0033365	0.343027	Protein localization to organelle (1)	3.0	1.0	0.239223
9	GO:0043408	0.476159	Regulation of mapk cascade (1)	2.0	1.0	0.238056
887	GO:0034504	0.294393	Protein localization to nucleus (1)	2.0	1.0	0.236099
598	GO:0043254	0.240098	Regulation of protein-containing complex assembly (1)	3.0	1.0	0.234151
280	GO:0006810	0.773180	Transport (1)	7.0	1.0	0.229528
267	GO:0045732	0.069485	Positive regulation of protein catabolic process (1)	2.0	1.0	0.220326
668	GO:0010976	0.084952	Positive regulation of neuron projection development (1)	1.0	1.0	0.216502
212	GO:0051090	0.131395	Regulation of dna-binding transcription factor activity (1)	2.0	1.0	0.215454
799	GO:0009743	0.121060	Response to carbohydrate (1)	2.0	1.0	0.214795
675	GO:0071277	0.074147	Cellular response to calcium ion (1)	0.0	1.0	0.211553
495	GO:0060976	0.094845	Coronary vasculature development (1)	1.0	1.0	0.210353
803	GO:0042475	0.125550	Odontogenesis of dentin-containing tooth (1)	2.0	1.0	0.208932
671	GO:0032092	0.120001	Positive regulation of protein binding (1)	1.0	1.0	0.204806
772	GO:0060485	0.320964	Mesenchyme development (1)	3.0	1.0	0.203758
121	GO:0090050	0.180206	Positive regulation of cell migration involved in sprouting angiogenesis (1)	0.0	1.0	0.202697
801	GO:0033143	0.078487	Regulation of intracellular steroid hormone receptor signaling pathway (1)	1.0	1.0	0.199503
870	GO:0070527	0.179383	Platelet aggregation (1)	1.0	1.0	0.197119
314	GO:0070588	0.238228	Calcium ion transmembrane transport (1)	4.0	1.0	0.196919
656	GO:0030154	0.829241	Cell differentiation (1)	6.0	1.0	0.193962
645	GO:0071300	0.128940	Cellular response to retinoic acid (1)	0.0	1.0	0.193772
897	GO:0035767	0.138830	Endothelial cell chemotaxis (1)	1.0	1.0	0.192330
332	GO:0097190	0.410188	Apoptotic signaling pathway (1)	4.0	1.0	0.189603
643	GO:0071222	0.143456	Cellular response to lipopolysaccharide (1)	2.0	1.0	0.189188
898	GO:0035924	0.211788	Cellular response to vascular endothelial growth factor stimulus (1)	2.0	1.0	0.184593
459	GO:0007267	0.506882	Cell-cell signaling (1)	5.0	1.0	0.184371
323	GO:0016236	0.245469	Macroautophagy (1)	3.0	1.0	0.183750
317	GO:0006897	0.277768	Endocytosis (1)	3.0	1.0	0.178141
244	GO:0042307	0.225164	Positive regulation of protein import into nucleus (1)	0.0	1.0	0.177873
728	GO:0030032	0.140303	Lamellipodium assembly (1)	1.0	1.0	0.163285
640	GO:0071310	0.661841	Cellular response to organic substance (1)	4.0	1.0	0.156044
686	GO:0097009	0.055448	Energy homeostasis (1)	0.0	1.0	0.155920
692	GO:0010595	0.235403	Positive regulation of endothelial cell migration (1)	2.0	1.0	0.154755
309	GO:0034765	0.277608	Regulation of ion transmembrane transport (1)	4.0	1.0	0.153881
770	GO:0009887	0.516411	Animal organ morphogenesis (1)	4.0	1.0	0.152678
209	GO:0006338	0.181855	Chromatin remodeling (1)	2.0	1.0	0.147943
547	GO:0032880	0.479479	Regulation of protein localization (1)	4.0	1.0	0.144928
918	GO:0046854	0.054745	Phosphatidylinositol phosphate biosynthetic process (1)	1.0	1.0	0.142413
329	GO:0043066	0.610305	Negative regulation of apoptotic process (1)	4.0	1.0	0.132954
824	GO:0071363	0.407544	Cellular response to growth factor stimulus (1)	3.0	1.0	0.122808
603	GO:0061045	0.068278	Negative regulation of wound healing (1)	2.0	1.0	0.122775
225	GO:0006470	0.305848	Protein dephosphorylation (1)	3.0	1.0	0.111956
388	GO:0051726	0.483775	Regulation of cell cycle (1)	5.0	1.0	0.109432
516	GO:0007423	0.372363	Sensory organ development (1)	3.0	1.0	0.106514
261	GO:0051171	0.836045	Regulation of nitrogen compound metabolic process (1)	6.0	1.0	0.100916
375	GO:0051496	0.076120	Positive regulation of stress fiber assembly (1)	0.0	1.0	0.097110
758	GO:0031099	0.247922	Regeneration (1)	2.0	1.0	0.096133
55	GO:0001649	0.136472	Osteoblast differentiation (1)	1.0	1.0	0.091104
227	GO:0032516	0.155024	Positive regulation of phosphoprotein phosphatase activity (1)	0.0	1.0	0.090968
191	GO:0045944	0.420524	Positive regulation of transcription by rna polymerase ii (1)	2.0	1.0	0.084844
283	GO:0051049	0.611523	Regulation of transport (1)	5.0	1.0	0.084359
739	GO:1902903	0.245529	Regulation of supramolecular fiber organization (1)	3.0	1.0	0.083258
569	GO:0030307	0.190101	Positive regulation of cell growth (1)	2.0	1.0	0.078819
679	GO:0042310	0.060677	Vasoconstriction (1)	1.0	1.0	0.075349
405	GO:0007159	0.162103	Leukocyte cell-cell adhesion (1)	3.0	1.0	0.074992
566	GO:0032879	0.660318	Regulation of localization (1)	6.0	1.0	0.074219
568	GO:0008284	0.513916	Positive regulation of cell population proliferation (1)	2.0	1.0	0.073069
561	GO:0048511	0.251652	Rhythmic process (1)	3.0	1.0	0.067632
702	GO:0048146	0.179210	Positive regulation of fibroblast proliferation (1)	0.0	1.0	0.067004
403	GO:0033628	0.059919	Regulation of cell adhesion mediated by integrin (1)	1.0	1.0	0.061975
885	GO:1900180	0.205300	Regulation of protein localization to nucleus (1)	1.0	1.0	0.060202
228	GO:0006508	0.347532	Proteolysis (1)	4.0	1.0	0.056727
560	GO:0043473	0.188137	Pigmentation (1)	2.0	1.0	0.036612
777	GO:0050680	0.103322	Negative regulation of epithelial cell proliferation (1)	2.0	1.0	0.036383
751	GO:0046034	0.094236	Atp metabolic process (1)	2.0	1.0	0.034832
591	GO:0010941	0.705559	Regulation of cell death (1)	5.0	1.0	0.026616
818	GO:0010243	0.557617	Response to organonitrogen compound (1)	4.0	1.0	0.022040
724	GO:0036473	0.154959	Cell death in response to oxidative stress (1)	2.0	1.0	0.021429
153	GO:0050776	0.424435	Regulation of immune response (1)	4.0	1.0	0.011047
521	GO:0035051	0.187551	Cardiocyte differentiation (1)	2.0	1.0	0.003905
194	GO:0006355	0.544373	Regulation of transcription, dna-templated (1)	4.0	1.0	0.003607
622	GO:0043086	0.212387	Negative regulation of catalytic activity (1)	3.0	1.0	-0.003920
494	GO:0060840	0.094489	Artery development (1)	2.0	1.0	-0.013518
936	GO:0051000	0.059868	Positive regulation of nitric-oxide synthase activity (1)	0.0	1.0	-0.017649
382	GO:0030041	0.093571	Actin filament polymerization (1)	2.0	1.0	-0.024295
712	GO:0033002	0.280632	Muscle cell proliferation (1)	2.0	1.0	-0.031117
750	GO:0044281	0.305872	Small molecule metabolic process (1)	5.0	1.0	-0.053702
257	GO:0046488	0.135113	Phosphatidylinositol metabolic process (1)	2.0	1.0	-0.069795
731	GO:0031529	0.036028	Ruffle organization (1)	1.0	1.0	-0.085835
469	GO:0060173	0.072132	Limb development (1)	1.0	1.0	-0.088184
296	GO:0015031	0.292221	Protein transport (1)	4.0	1.0	-0.098692
927	GO:0042632	0.034870	Cholesterol homeostasis (1)	0.0	1.0	-0.119705
78	GO:0032760	0.100402	Positive regulation of tumor necrosis factor production (1)	0.0	1.0	-0.132748
856	GO:0019722	0.034146	Calcium-mediated signaling (1)	2.0	1.0	-0.141431
288	GO:0032940	0.217398	Secretion by cell (1)	5.0	1.0	-0.147194
705	GO:0051353	0.082787	Positive regulation of oxidoreductase activity (1)	1.0	1.0	-0.158575
176	GO:0044262	0.129234	Cellular carbohydrate metabolic process (1)	3.0	1.0	-0.190099
180	GO:0019318	0.046251	Hexose metabolic process (1)	2.0	1.0	-0.211363
440	GO:0030512	0.045444	Negative regulation of transforming growth factor beta receptor signaling pathway (1)	0.0	1.0	-0.229806
331	GO:0071887	0.072964	Leukocyte apoptotic process (1)	2.0	1.0	-0.295526
823	GO:0045471	0.068117	Response to ethanol (1)	1.0	1.0	-0.639920
232	GO:0010951	0.025246	Negative regulation of endopeptidase activity (1)	2.0	1.0	-0.917276

names2 = list(probabilities_mod.loc[probabilities_mod["predictions"] ==1].loc[probabilities_mod["layer_number"] <=7].sort_values(by=["delta_logits"], ascending=False)["Name"].head(30))
terms2 = list(probabilities_mod.loc[probabilities_mod["predictions"] ==1].loc[probabilities_mod["layer_number"] <=7].sort_values(by=["delta_logits"], ascending=False)["GO_term"].head(30))
logits2 = list(probabilities_mod.loc[probabilities_mod["predictions"] ==1].loc[probabilities_mod["layer_number"] <=7].sort_values(by=["delta_logits"], ascending=False)["delta_logits"].head(30))

names2 = [x[:-4] for x in names2] 

for i in range(0,len(names2)):
    print(terms2[i],names2[i],logits2[i])

GO:0031047 Gene silencing by rna 2.192457619336144
GO:0031648 Protein destabilization 2.092833916210919
GO:0071900 Regulation of protein serine/threonine kinase activity 1.944569179670069
GO:0000423 Mitophagy 1.8800065969407627
GO:0048266 Behavioral response to pain 1.8570307139212263
GO:0090630 Activation of gtpase activity 1.793943480641404
GO:0045727 Positive regulation of translation 1.7460704310285706
GO:0018108 Peptidyl-tyrosine phosphorylation 1.6728257190053135
GO:0043244 Regulation of protein-containing complex disassembly 1.6460980615310405
GO:0006612 Protein targeting to membrane 1.6268405228374492
GO:0006469 Negative regulation of protein kinase activity 1.5937943848967007
GO:0034976 Response to endoplasmic reticulum stress 1.575980420814227
GO:0071353 Cellular response to interleukin-4 1.5752555355925209
GO:0033554 Cellular response to stress 1.573975479674923
GO:0033993 Response to lipid 1.5515734212063519
GO:0001817 Regulation of cytokine production 1.5347154822720621
GO:0007026 Negative regulation of microtubule depolymerization 1.5337813437727923
GO:0007565 Female pregnancy 1.4788810435976616
GO:0046777 Protein autophosphorylation 1.453821209026271
GO:0060179 Male mating behavior 1.4409006464912832
GO:0000422 Autophagy of mitochondrion 1.4098807478143418
GO:0030216 Keratinocyte differentiation 1.406529668352229
GO:0048812 Neuron projection morphogenesis 1.3850039160074457
GO:0001553 Luteinization 1.3733255324441331
GO:0002718 Regulation of cytokine production involved in immune response 1.3728274508963876
GO:0042060 Wound healing 1.325910525701464
GO:0060632 Regulation of microtubule-based movement 1.3061039503079361
GO:0065003 Protein-containing complex assembly 1.304383769510548
GO:0060020 Bergmann glial cell differentiation 1.2886961413918279
GO:1902459 Positive regulation of stem cell population maintenance 1.2787216247479065

# import libraries
import pandas as pd
import matplotlib.pyplot as plt
import numpy as np
%matplotlib inline

# set font
plt.rcParams['font.family'] = 'sans-serif'
plt.rcParams['font.sans-serif'] = 'Roboto'

# set the style of the axes and the text color
plt.rcParams['axes.edgecolor']='#333F4B'
plt.rcParams['axes.linewidth']=0.8
plt.rcParams['xtick.color']='#333F4B'
plt.rcParams['ytick.color']='#333F4B'
plt.rcParams['text.color']='#333F4B'


# create some fake data
percentages = pd.Series(logits2, 
                        index=names2)
df = pd.DataFrame({'percentage' : percentages})
df = df.sort_values(by='percentage')

# we first need a numeric placeholder for the y axis
my_range=list(range(1,len(df.index)+1))

fig, ax = plt.subplots(figsize=(4,17))

# create for each expense type an horizontal line that starts at x = 0 with the length 
# represented by the specific expense percentage value.
plt.hlines(y=my_range, xmin=0, xmax=df['percentage'], color='#208EA3', alpha=0.2, linewidth=14)

# create for each expense type a dot at the level of the expense percentage value
plt.plot(df['percentage'], my_range, "o", markersize=14, color='#208EA3', alpha=0.8)

# set labels
ax.set_xlabel(' Δlogit', fontsize=25, fontweight='black', color = '#36382E')
ax.set_ylabel('')
ax.set_facecolor(color="white")
ax.set_alpha(1)

# set axis
ax.tick_params(axis='both', which='major', labelsize=30)
plt.yticks(my_range, df.index)

# add an horizonal label for the y axis 
fig.text(-0.58, 0.862, 'MoA (GO terms)', fontsize=27, fontweight='black', color = '#36382E')
fig.text(0.2, 0.9, selected_drug_u_name.capitalize(), fontsize=30, fontweight='black', color = '#36382E')


# change the style of the axis spines
ax.spines['top'].set_visible(False)
ax.spines['right'].set_visible(False)

ax.spines['left'].set_bounds((1, len(my_range)))
ax.set_xlim(0,max(logits2)+0.1)

ax.spines['left'].set_position(('outward', 8))
ax.spines['bottom'].set_position(('outward', 5))

plt.savefig(resultsdir+selected_drug_u_name+'_top_terms.png', dpi=300, bbox_inches='tight')

For known drug…

display(combobox)

selected_drug_name = combobox.result

# LOS LOGITS DE TEST!!
train_drug_logs = pd.DataFrame(platt_matrix.loc[:,selected_drug_name]).reset_index()
train_drug_logs.columns  = ["GO_term","probability"]
train_drug_logs = train_drug_logs.merge(real_go_info_svm, on="GO_term")

train_drug_logs.loc[train_drug_logs["layer_number"] <=3].sort_values(by=["probability"], ascending=False).head(30)

# # For known drugs
len((set(train_drug_logs.loc[train_drug_logs["layer_number"] <=3].sort_values(by=["probability"], ascending=False).head(30)["GO_term"])).intersection(set(pd.DataFrame(compounds_GOterms_matches[selected_drug_name])[1])))

30

ax = sns.boxplot(x=slim_matrix_single_neuron.loc[train_drug_logs["GO_term"],selected_drug_name], y=train_drug_logs.set_index("GO_term")["probability"], data=plot,showfliers=True )

# same as before
sum_annotations = slim_matrix_single_neuron.T.sum()/slim_matrix_single_neuron.shape[1]
logits_apriori= np.log(sum_annotations/(1-sum_annotations))

logits_apost= np.log(train_drug_logs["probability"]/(1-train_drug_logs["probability"]))
delta_logits = logits_apost.to_numpy()-logits_apriori.to_numpy()
delta_logits_df = pd.DataFrame(delta_logits)
delta_logits_df.columns = ["delta_logits"]
train_drug_mod = train_drug_logs.merge(delta_logits_df, left_index=True,right_index=True)

train_drug_mod.loc[train_drug_mod["layer_number"] <=3].sort_values(by=["delta_logits"], ascending=False).head(30)

	GO_term	probability	Name	layer_number	delta_logits
578	GO:2000379	0.603184	Positive regulation of reactive oxygen species metabolic process (1)	1.0	2.770126
253	GO:0043552	0.735601	Positive regulation of phosphatidylinositol 3-kinase activity (1)	0.0	2.707570
80	GO:0010575	0.438910	Positive regulation of vascular endothelial growth factor production (1)	0.0	2.282492
633	GO:0051301	0.817491	Cell division (1)	2.0	2.245231
224	GO:0046777	0.780375	Protein autophosphorylation (1)	1.0	2.053782
423	GO:1902533	0.825532	Positive regulation of intracellular signal transduction (1)	2.0	2.014408
458	GO:0035025	0.374269	Positive regulation of rho protein signal transduction (1)	0.0	1.952270
848	GO:0071670	0.345148	Smooth muscle cell chemotaxis (1)	0.0	1.887644
348	GO:0006939	0.412178	Smooth muscle contraction (1)	2.0	1.842256
350	GO:0045987	0.348868	Positive regulation of smooth muscle contraction (1)	1.0	1.783402
908	GO:0051899	0.591286	Membrane depolarization (1)	2.0	1.675960
653	GO:0072593	0.631785	Reactive oxygen species metabolic process (1)	3.0	1.650121
115	GO:0060312	0.275354	Regulation of blood vessel remodeling (1)	0.0	1.625762
926	GO:0060020	0.369360	Bergmann glial cell differentiation (1)	0.0	1.614859
923	GO:0048170	0.295742	Positive regulation of long-term neuronal synaptic plasticity (1)	0.0	1.598558
99	GO:0001934	0.823688	Positive regulation of protein phosphorylation (1)	3.0	1.593723
853	GO:0038083	0.500000	Peptidyl-tyrosine autophosphorylation (1)	0.0	1.588712
713	GO:0035726	0.210551	Common myeloid progenitor cell proliferation (1)	0.0	1.577983
445	GO:0048008	0.414654	Platelet-derived growth factor receptor signaling pathway (1)	1.0	1.552362
857	GO:0035584	0.298388	Calcium-mediated signaling using intracellular calcium source (1)	0.0	1.496390
333	GO:1904019	0.457451	Epithelial cell apoptotic process (1)	1.0	1.481165
933	GO:0051150	0.337253	Regulation of smooth muscle cell differentiation (1)	1.0	1.474264
352	GO:0014827	0.254529	Intestine smooth muscle contraction (1)	0.0	1.453480
814	GO:0090037	0.235572	Positive regulation of protein kinase c signaling (1)	0.0	1.416277
894	GO:0048017	0.609803	Inositol lipid-mediated signaling (1)	1.0	1.399588
506	GO:0007286	0.240113	Spermatid development (1)	1.0	1.376021
742	GO:0035733	0.239304	Hepatic stellate cell activation (1)	0.0	1.371582
10	GO:0051403	0.429898	Stress-activated mapk cascade (1)	2.0	1.369506
702	GO:0048146	0.440487	Positive regulation of fibroblast proliferation (1)	0.0	1.349525
782	GO:1902042	0.289337	Negative regulation of extrinsic apoptotic signaling pathway via death domain receptors (1)	0.0	1.347889

ax = sns.boxplot(x=slim_matrix_single_neuron.loc[train_drug_mod["GO_term"],selected_drug_name], y=train_drug_mod.set_index("GO_term")["delta_logits"], data=plot,showfliers=True)

SVM GO TERM 2D representation

from sklearn.manifold import TSNE
import plotly.express as px

Choose go to study…

display(combobox_go)

selected_goterm = combobox_go.result

real_go_info[real_go_info["GO_term"]==selected_goterm+"_1"]

	GO_term	Name	layer_number
4338	GO:0071353_1	Cellular response to interleukin-4 (1)	1.0

list_nodes = []
for i in range(1,7):
    list_nodes.append(selected_goterm+"_"+str(i))

score = attribution_data_annotated.loc[list_nodes].T
score_mod = score.divide(score.std()).fillna(0) 
annotations  =slim_matrix_single_neuron.loc[selected_goterm,]
y_predicted = models_svm[selected_goterm].predict(score_mod.astype(float))

Plot SVM

View statistics of GOterm

“Perfect” model (with train data)

auc = metrics.roc_auc_score(annotations, models_svm[selected_goterm].decision_function(score_mod.astype(float)))
cnf_matrix = metrics.confusion_matrix(annotations,y_predicted)
print(cnf_matrix)

print("Accuracy:",metrics.accuracy_score(annotations, y_predicted))
print("Precision:",metrics.precision_score(annotations,y_predicted)) # TP / (TP+FP)
print("Recall:",metrics.recall_score(annotations, y_predicted)) #TP / (TP+FN)
print("AUC with score:",auc) 

[[206   6]
 [  2  16]]
Accuracy: 0.9652173913043478
Precision: 0.7272727272727273
Recall: 0.8888888888888888
AUC with score: 0.9855870020964361

TN - FP

FN - TP

En mi opinion interesa mucho el precision, prefiero que haya menos FP no??

Test statistics…

auc = metrics.roc_auc_score(slim_matrix_single_neuron.loc[selected_goterm],  platt_matrix.loc[selected_goterm])
cnf_matrix = metrics.confusion_matrix(slim_matrix_single_neuron.loc[selected_goterm], preds_svm_matrix.loc[selected_goterm])
print(cnf_matrix)

print("Accuracy:",metrics.accuracy_score(slim_matrix_single_neuron.loc[selected_goterm], preds_svm_matrix.loc[selected_goterm]))
print("Precision:",metrics.precision_score(slim_matrix_single_neuron.loc[selected_goterm], preds_svm_matrix.loc[selected_goterm]))
print("Recall:",metrics.recall_score(slim_matrix_single_neuron.loc[selected_goterm], preds_svm_matrix.loc[selected_goterm])) #TP / (TP+FN)
print("AUC with score:",auc) #TP / (TP+FN)

[[203   9]
 [  4  14]]
Accuracy: 0.9434782608695652
Precision: 0.6086956521739131
Recall: 0.7777777777777778
AUC with score: 0.9095911949685536

import colorlover as cl
matrix = metrics.confusion_matrix(annotations,y_predicted)
tn, fp, fn, tp = matrix.ravel()

values = [tp, fn, fp, tn]
label_text = ["True Positive", "False Negative", "False Positive", "True Negative"]
labels = ["<b>TP</b>", "<b>FN</b>", "<b>FP</b>", "<b>TN</b>"]
blue = cl.flipper()["seq"]["9"]["Blues"]
red = cl.flipper()["seq"]["9"]["Reds"]
colors = ["#ff3700","#FFA0A0", "#CCE9FF",  "#0b8bff"]
trace0 = go.Pie(
    labels=label_text,
    values=values,
    hoverinfo="label+value+percent",
    textinfo="text+value",
    text=labels,
    sort=False,
    marker=dict(colors=colors),
    insidetextfont={"color": "#36382E"},
    rotation=90,
)

layout = go.Layout(
    title=dict(text="Confusion Matrix",
              x=0.3,
              y=0.8,
              font=dict(size=14),
              xanchor='center',
              yanchor='top'),
    #margin=dict(l=50, r=50, t=100, b=10),
    legend=dict(font={"color": "#36382E"}, orientation="h",x=0.1, y=-0.03),
#    plot_bgcolor="#282b38",
#    paper_bgcolor="#282b38",
    font=dict(family='Roboto',color= "#36382E"),
)

data = [trace0]
figure = go.Figure(data=data, layout=layout)
figure

y_test=annotations
decision_test=y_predicted
fpr, tpr, threshold = metrics.roc_curve(y_test, decision_test)

# AUC Score
auc_score = metrics.roc_auc_score(y_true=y_test, y_score=decision_test)

trace0 = go.Scatter(
    x=fpr, y=tpr, mode="lines", name="Test Data", marker={"color": "#ff3700"}
)

layout = go.Layout(
    title=dict(text=f"ROC Curve (AUC = {auc_score:.3f})",
            x=0.6,
            y=0.5,
            font=dict(size=20)
              ),
    xaxis=dict(title="False Positive Rate", gridcolor="white"),
    yaxis=dict(title="True Positive Rate", gridcolor="white"),
    legend=dict(x=0, y=1.05, orientation="h"),
    margin=dict(l=100, r=10, t=25, b=40),
#    plot_bgcolor="#282b38",
#    paper_bgcolor="#282b38",
    font=dict(family='Roboto',color= "#36382E"),
)

data = [trace0]
figure = go.Figure(data=data, layout=layout)
figure

Plot SVM with unknown labels

Voronoi Tessellation

What is a Voronoi Tessellation? Given a set P := {p1, …, pn} of sites, a Voronoi Tessellation is a subdivision of the space into n cells, one for each site in P, with the property that a point q lies in the cell corresponding to a site pi iff d(pi, q) < d(pj, q) for i distinct from j. The segments in a Voronoi Tessellation correspond to all points in the plane equidistant to the two nearest sites. Voronoi Tessellations have applications in computer science.

https://stackoverflow.com/questions/61225052/svm-plot-decision-surface-when-working-with-more-than-2-features

tsne = TSNE(n_components=2, verbose=0,
           init="pca",
            perplexity=30,
            random_state=123
           )
z = tsne.fit_transform(score_mod.astype(float)) 

list_nodes = list(models_svm[selected_goterm].feature_names_in_) # Extract the feature names from the model (those are the attributions we need)
score_unknown = attribution_data_all.loc[list_nodes,unknown].T
score_unknown_mod = score_unknown.divide(score.std()).fillna(0) # normalize

y_unknown = np.full(score_unknown_mod.shape[0],2) # 2=unknown MOA
y_pred_unknown = models_svm[selected_goterm].predict(score_unknown_mod.astype(float))

# join scores and annotations from known and unknown drugs
all_score = pd.concat([score_mod,score_unknown_mod])
all_y = np.concatenate((annotations,y_unknown))  # 2=unknown MOA

Plot T-SNE SVM

from sklearn.neighbors._classification import KNeighborsClassifier
# https://github.com/plotly/dash-sample-apps/blob/main/apps/dash-svm/utils/dash_reusable_components.py
z = tsne.fit_transform(all_score.astype(float)) 
df = pd.DataFrame()
df["y"] = all_y
df["comp-1"] = z[:,0]
df["comp-2"] = z[:,1]
df["name"] =list(all_score.index)
df = df.sort_values(by=['y'])
df["y"] = df["y"].astype(str)
X,y = all_score.astype(float), all_y
y_predicted = models_svm[selected_goterm].predict(X)

resolution = 300 # 100x100 background pixels
X2d_xmin, X2d_xmax = np.min(z[:,0])-1, np.max(z[:,0])+1
X2d_ymin, X2d_ymax = np.min(z[:,1])-1, np.max(z[:,1])+1
xx, yy = np.meshgrid(np.linspace(X2d_xmin, X2d_xmax, resolution), np.linspace(X2d_ymin, X2d_ymax, resolution))

# approximate Voronoi tesselation on resolution x resolution grid using 1-NN
background_model = KNeighborsClassifier(n_neighbors=1).fit(z, y_predicted) 
voronoiBackground = background_model.predict(np.c_[xx.ravel(), yy.ravel()])
voronoiBackground = voronoiBackground.reshape((resolution, resolution))

go_name=real_go_info[real_go_info["GO_term"]==selected_goterm+"_1"]["Name"].values[0][:-4]
go_name

'Cellular response to interleukin-4'

bright_cscale = [[0, "#0b8bff"], [0.5, "#ff3700"],[1, "#36382E"]]
new_cscale = [[0, "#CCE9FF"], [1, "#FFA0A0"]]

trace0 = go.Contour(
        x=xx.flatten(),
        y=yy.flatten(),
        z=voronoiBackground.flatten(),
        hoverinfo="none",
        showscale=False,
        contours=dict(showlines=False),
        colorscale=new_cscale,
        opacity=0.9,
    )
    
trace1 = go.Contour(
    x=xx.flatten(),
    y=yy.flatten(),
    z=voronoiBackground.flatten(),
    showscale=False,
    hoverinfo="none",
    colorscale=new_cscale,
    line=dict(color="#ff3700"),
    )

trace2 = go.Scatter(
    x=df["comp-1"],
    y=df["comp-2"],
    mode="markers",
    text=df["name"].to_numpy(),
    marker=dict(size=7, color=df["y"].to_numpy(int),colorscale=bright_cscale),
    showlegend=False
)

legend1 = go.Scatter(
    x=[None],
    y=[None],
    mode="markers",
    name="Not annotated to<br>"+selected_goterm,
    marker=dict(size=7, color="#0b8bff",symbol='circle'),
)

legend2 = go.Scatter(
    x=[None],
    y=[None],
    mode="markers",
    name="Drug annotated to<br>"+selected_goterm,
    marker=dict(size=7, color="#ff3700",symbol='circle'),
)

legend3 = go.Scatter(
    x=[None],
    y=[None],
    mode="markers",
    name="Unknown MOA<br>annotations",
    marker=dict(size=7, color="#36382E",symbol='circle'),
)

    
layout = go.Layout(
   title=dict(text="<b>"+selected_goterm+"</b> "+go_name,
              x=0.5,
              y=0.92,
              font=dict(size=18),
              xanchor='center',
              yanchor='top'),
    xaxis=dict(ticks="", showticklabels=False, showgrid=False, zeroline=False),
    yaxis=dict(ticks="", showticklabels=False, showgrid=False, zeroline=False),
    yaxis_range=[min(yy.flatten()),max(yy.flatten())],
    xaxis_range=[min(xx.flatten()),max(xx.flatten())],
    legend=dict(x=0, y=0, orientation="h",font=dict(size=14)),
    paper_bgcolor='rgba(0,0,0,0)',
    width=600, height=800,
    font=dict(family='Roboto',color= "#36382E",size=15)
    )
data = [trace0,trace1,trace2,legend2,legend1,legend3]
figure = go.Figure(data=data,layout=layout)




figure